WO2001004828A1 - Automated detection of objects in a biological sample - Google Patents

Abstract

A method, system (Fig. 2), and apparatus are provided for automated light microscopic (fig. 2, 31) for detection of proteins associated with cell proliferative disorders.

Description

AUTOMATED DETECTION OF OBJECTS IN A BIOLOGICAL SAMPLE

CLAIM OF PRIORITY

This application claims the benefit of priority,

under 35 USC §119 (e) (1), of U.S. provisional

application 60/143,823, filed July 13, 1999, and this

application is a continuation of U.S. Patent

Application Serial No. 08/758,436, filed November 27,

1996, which claims the benefit of U.S. Provisional

Application No. 60/026,805, filed on November 30,

1995.

TECHNICAL FIELD

The invention relates generally to light

microscopy and, more particularly, to automated light

microscopic methods and an apparatus for detection of

proteins associated with cell proliferative

disorders . BACKGROUND OF THE INVENTION

In the field of medical diagnostics including

oncology, the detection, identification, quantitation

and characterization of cells of interest, such as

cancer cells, through testing of biological specimens

is an important aspect of diagnosis. Typically, a

biological specimen such as bone marrow, lymph nodes,

peripheral blood, cerebrospinal fluid, urine,

effusions, fine needle aspirates, peripheral blood

scrapings or other materials are prepared by staining

the specimen to identify cells of interest. One

method of cell specimen preparation is to react a

specimen with a specific probe which can be a

monoclonal antibody, a polyclonal antiserum, or a

nucleic acid which is reactive with a component of

the cells of interest, such as tumor cells. The

reaction may be detected using an enzymatic reaction,

such as alkaline phosphatase or glucose oxidase or

peroxidase to convert a soluble colorless substrate

to a colored insoluble precipitate, or by directly

conjugating a dye to the probe. Examination of biological specimens in the past has been performed

manually by either a lab technician or a pathologist.

In the manual method, a slide prepared with a

biological specimen is viewed at a low magnification

under a microscope to visually locate candidate cells

of interest. Those areas of the slide where cells of

interest are located are then viewed at a higher

magnification to confirm those objects as cells of

interest, such as tumor or cancer cells. The manual

method is time consuming and prone to error including

missing areas of the slide. Automated cell analysis

systems have been developed to improve the speed and

accuracy of the testing process. One known

interactive system includes a single high power

microscope objective for scanning a rack of slides,

portions of which have been previously identified for

assay by an operator. In that system, the operator

first scans each slide at a low magnification similar

to the manual method and notes the points of interest

on the slide for later analysis. The operator then

stores the address of the noted location and the associated function in a data file. Once the points

of interest have been located and stored by the

operator, the slide is then positioned in an

automated analysis apparatus which acquires images of

the slide at the marked points and performs an image

analysis.

A number of cellular proteins are related to

cell proliferation and cell signaling. Many of these

proteins are critical for normal cell growth. For

example, HER2 (neu) is a growth factor receptor and

when found within tumor cells amounts to an

aggressively growing tumor. Studies have determined

that a significantly decreased disease-free survival

and overall survival of a patient with over-

expression of HER2. Before an oncologist prescribes

an anti-HER2/neu therapeutic, an immunohistochemistry

(IHC) assessment for HER2/neu is desirable.

Therapeutic availability increases the need for a

standard methodology for assessing the expression of

HER2/neu. Three general methods are currently available

for the detection of HER2/neu: genetic detection,

protein expression, and protein activity. In situ

hybridization methods are typically used for HER2/neu

genetic detection. Immunohistochemistry methods are

used for the assessment of HER2/neu protein

expression.

SUMMARY OF THE INVENTION

A problem with the foregoing automated system is

the continued need for operator input to initially

locate cell objects for analysis. Such continued

dependence on manual input can lead to errors

including cells of interest being missed. Such

errors can be critical especially in assays for so-

called rare events, e.g., finding one tumor cell in a

cell population of one million normal cells.

Additionally, manual methods can be extremely time

consuming and can require a high decree of training

to identify and/or quantify cells . This is not only

true for tumor cell detection, but also for other applications ranging from neutrophil alkaline

phosphatase assays, reticulocyte counting and

maturation assessment, and others. The associated

manual labor leads to a high cost for these

procedures in addition to the potential errors that

can arise from long, tedious manual examinations. A

need exists, therefore, for an improved automated

cell analysis system which can quickly and accurately

scan large amounts of biological material on a slide.

Accordingly, the present invention provides a method

and apparatus for automated cell analysis which

eliminates the need for operator input to locate cell

objects for analysis.

In accordance with the present invention, a

slide prepared with a biological specimen and reagent

is placed in a slide carrier which preferably holds

four slides. The slide carriers are loaded into an

input hopper of the automated system. The operator

may then enter data identifying the size, shape, and

location of a scan area on each slide, or,

preferably, the system automatically locates a scan area for each slide during slide processing. The

operator then activates the system for slide

processing. At system activation, a slide carrier is

positioned on an X-Y stage of an optical system. Any

bar codes used to identify slides are then read and

stored for each slide in a carrier. The entire slide

is rapidly scanned at a low magnification, typically

lOx. At each location of the scan, a low

magnification image is acquired and processed to

detect candidate objects of interest. Preferably,

color, size, and shape are used to identify objects

of interest. The location of each candidate object

of interest is stored.

At the completion of the low level scan for each

slide in the carrier on the stage, the optical system

is adjusted to a high magnification such as 40x or

60x, and the X-Y stage is positioned to the stored

locations for the candidate objects of interest on

each slide in the carrier. A high magnification

image is acquired for each candidate object of

interest and a series of image processing steps are performed to confirm the analysis which was performed

at low magnification. A high magnification image is

stored for each confirmed object of interest.

Additionally, control slides including positive

and negative controls, may be used to determine

background staining. For example, positive control

slides for a particular staining technique can be run

followed by a negative control in order to determine

a delta for the controls . Subsequent scanning for

objects of interest may then differentiate such

objects based upon their color or intensity above the

delta.

These images are then available for retrieval by

a pathologist or cytotechnologist to review for final

diagnostic evaluation. Having stored the location of

each object of interest, a mosaic comprised of the

candidate objects of interest for a slide may be

generated and stored. The pathologist or

cytotechnologist may view the mosaic or may also

directly view the slide at the location of an object

of interest in the mosaic for further evaluation. The mosaic may be stored on magnetic media for future

reference or may be transmitted to a remote site for

review and/or storage. The entire process involved

in examining a single slide takes on the order of 2-

15 minutes depending on scan area size and the number

of detected candidate objects of interest.

The present invention has utility in the field

of oncology for the early detection of minimal

residual disease ( ^λ 'micrometastases" ) . Other useful

applications include prenatal diagnosis of fetal

cells in maternal blood and in the field of

infectious diseases to identify pathogens and viral

loads, alkaline phosphatase assessments, reticulocyte

counting, and others. The processing of images

acquired in the automated scanning of the present

invention preferably includes the steps of

transforming the image to a different color space;

filtering the transformed image with a low pass

filter; dynamically thresholding the pixels of the

filtered image to suppress background material;

performing a morphological function to remove artifacts from the thresholded image; analyzing the

thresholded image to determine the presence of one or

more regions of connected pixels having the same

color; and categorizing every region having a size

greater than a minimum size as a candidate object of

interest .

According to another aspect of the invention,

the scan area is automatically determined by scanning

the slide; acquiring an image at each slide position;

analyzing texture information of each image to detect

the edges of the specimen; and storing the locations

corresponding to the detected edges to define the

scan area . According to yet another aspect of the

invention, automated focusing of the optical system

is achieved by initially determining a focal plane

from an array of points or locations in the scan

area . The derived focal plane enables subsequent

rapid automatic focusing in the low power scanning

operation. The focal plane is determined by

determining proper focal positions across an array of

locations and performing an analysis such as a least squares fit of the array of focal positions to yield

a focal plane across the array. Preferably, a focal

position at each location is determined by

incrementing the position of a Z stage for a fixed

number of coarse and fine iterations . At each

iteration, an image is acquired and a pixel variance

or other optical parameter about a pixel mean for the

acquired image is calculated to form a set of

variance data. A least squares fit is performed on

the variance data according to a known function. The

peak value of the least squares fit curve is selected

as an estimate of the best focal position.

In another aspect of the present invention,

another focal position method for high magnification

locates a region of interest centered about a

candidate object of interest within a slide which

were located during an analysis of the low

magnification images. The region of interest is

preferably n columns wide, where n is a power of 2.

The pixels of this region are then processed using a

Fast Fourier Transform to generate a spectra of component frequencies and corresponding complex

magnitude for each frequency component . Magnitudes

of the frequency components which range from 25% to

75% of the maximum frequency component are squared

and summed to obtain the total power for the region

of interest. This process is repeated for other Z

positions and the Z position corresponding to the

maximum total power for the region of interest is

selected as the best focal position.

According to still another aspect of the

invention, a method and apparatus for automated slide

handling is provided. A slide is mounted onto a

slide carrier with a number of other slides side-by-

side. The slide carrier is positioned in an input

feeder with other slide carriers to facilitate

automatic analysis of a batch of slides. The slide

carrier is loaded onto the X-Y stage of the optical

system for the analysis of the slides thereon.

Subsequently, the first slide carrier is unloaded

into an output feeder after automatic image analysis

and the next carrier is automatically loaded.

In a specific embodiment the invention provides

an automated system for the quantitation of proteins

associated with cell proliferative disorders, such as

HER2/neu expression in tissue. The invention is

useful to determine the over-expression of HER2 in

tissue, especially breast tissue.

DESCRIPTION OF THE DRAWINGS

The above and other features of the invention

including various novel details of construction and

combinations of parts will now be more particularly

described with reference to the accompanying drawings

and pointed out in the claims . It will be understood

that the particular apparatus embodying the invention

is shown by way of illustration only and not as a

limitation of the invention. The principles and

features of this invention may be employed in varied

and numerous embodiments without departing from the

scope of the invention. Fig. 1 is a perspective view of an apparatus for

automated cell analysis embodying the present

invention.

Fig. 2 is a block diagram of the apparatus shown

in Fig. 1.

Fig. 3 is a block diagram of the microscope

controller of Fig. 2.

Fig. 4 is a plan view of the apparatus of Fig. 1

having the housing removed.

Fig. 5 is a side view of a microscope subsystem

of the apparatus of Fig . 1.

Fig. 6a is a top view of a slide carrier for use

with the apparatus of Fig. 1.

Fig. 6b is a bottom view of the slide carrier of

Fig. 6a.

Fig. 7a is a top view of an automated slide

handling subsystem of the apparatus of Fig. 1.

Fig. 7b is a partial cross-sectional view of the

automated slide handling subsystem of Fig. 7a taken

on line A-A. Fig. 8 is an end view of the input module of the

automated slide handling subsystem.8a-8d illustrate

the input operation of the automatic slide handling

subsystem.

Fig. 9a-9d illustrate the output operation of

the automated slide handling subsystem.

Fig. 10 is a flow diagram of the procedure for

automatically determining a scan area.

Fig. 11 shows the scan path on a prepared slide

in the procedure of Fig. 10.

Fig. 12 illustrates an image of a field acquired

in the procedure of Fig. 10.

Fig. 13A is a flow diagram of a preferred

procedure of redetermining focal position.

Fig. 13B is a flow diagram of a preferred

procedure for determining a focal position for

neutrophils stained with Fast Red and counterstained

with hemotoxylin.

Fig. 14 is a flow diagram of a procedure for

automatically determining initial focus. Fig. 15 shows an array of slide positions for

use in the procedure of Fig. 14.

Fig. 16 is a flow diagram of a procedure for

automatic focusing at a high magnification.

Fig. 17A is a flow diagram of an overview of the

preferred process to locate and identify objects of

interest in a stained biological specimen on a slide.

Fig. 17B is a flow diagram of a procedure for

color space conversion.

Fig. 18 is a flow diagram of a procedure for

background suppression via dynamic thresholding.

Fig. 19 is a flow diagram of a procedure for

morphological processing.

Fig. 20 is a flow diagram of a procedure for

blob analysis.

Fig. 21 is a flow diagram of a procedure for

image processing at a high magnification.

Fig. 22 illustrates a mosaic of cell images

produced by the apparatus . Fig. 23 is a flow diagram of a procedure for

estimating the number of nucleated cells in an

objective field.

Fig. 24 illustrates the apparatus functions

available in a user interface of the apparatus .

DETAILED DESCRIPTION

Overview

The invention provides an automated tissue image

analysis method for the detection of a cell

proliferative disorder, for example those associated

with HER2/neu. Other automated tissue image analysis

methods, such as immunohistochemical analyses of

estrogen receptor and progesterone receptor are also

provided. Other methods are within the scope of the

invention, such as analyses for MM/MRD for tissue,

MIB-I, Microvessel density analysis, the oncogene

p53, immunophenotyping, hematoxylin/eosin (H/E)

morphological analysis, undifferentiated tumor

classification, antibody titering, prominence of

nucleoli, HIV p24, human papiloma virus (HPV; for cervical biopsy) , and mitotic index; generally any

diagnostic method utilizing staining techniques is

within the scope of the present invention. The

invention has utility in the field of oncology for

the early detection of minimal residual disease

( ^λ,micrometastases" ) .

For the HER2/neu application, the Automated

Cellular Imaging System (ACIS™) analyzes at least two

subsamples : the H/E prepared slide and the

immunohistochemistry prepared slide. A histological

reconstruction of subsamples is displayed and used

for the appropriate reviewing. An instrument

automatically selects an area of interest, using

color space transformations (and morphological

filtering, if necessary) to identify and quantify the

expression of the HER2/neu in the area of tissue

selected from the immunohistochemistry prepared

slide. The quantitative result, as well as any

selected areas of interest from either slide, is

incorporated into the patient report . The following acronyms and terminology are used

throughout this document: A worklist is a group of

specimens that is prepared with the same staining

techniques and typically analyzed with the same

automated application. A specimen group consists of

one or more patient cases . A histological

reconstruction is an image of the whole specimen that

has been mounted on a slide. This image is created

by piecing together more than one fields of view at

any objective. Unless otherwise defined, all

technical and scientific terms used herein have the

same meaning as commonly understood by one of

ordinary skill in the art to which this invention

belongs .

Although methods and materials similar or

equivalent to those described herein can be used in

the practice or testing of the present invention,

suitable methods and materials are described below.

All publications, patent applications, patents,

and other references mentioned herein are

incorporated by reference in their entirety. In case of conflict, the present application, including

definitions, will control. In addition, the

materials and methods described herein are

illustrative only and not intended to be limiting.

Other features and advantages of the invention will

be apparent from the following detailed description,

the drawings, and from the claims.

Preferred Workflow

A preferred workflow is provided in the

following outline:

1. Specimens arrive in the laboratory. The

specimens are labeled in accordance with

standard hospital procedures to insure the

chain of custody.

2. Sample Preparation.

2.1. Two or three tissue sections per

patient case are preferable for this

test .

2.2. Mount one piece of the tissue to a

slide, to be stained with H/E. The H/E staining protocol is compatible with

HER2/neu immunohistochemistry staining.

2.3. Mount the second piece of the tissue to

a slide, to be stained with the anti-

HER2 immunohistochemistry staining

system.

2.4. If deemed necessary, mount a third

piece of the tissue to a third slide,

to be stained with a third antibody

(i.e., patient negative control) and

the anti-HER2 staining system.

2.5. To avoid ACIS™ mechanical limitations,

the specimen placement specifications

is followed. Considering the frosted

end of the slide as the top, specimen

must be below 30 mm and above 65 mm

from the top edge . The specimen may

reach to the far side edges of the

slide.

2.6. Other patient samples? If yes, repeat

section 2. 2.7. Include two stain control slides.

3. Sample Staining.

3.1. Stain all H/E prepared slides with the

H/E staining protocol .

3.2. Stain all immunohistochemistry slides

with anti-HER2 staining system.

3.3. If deemed necessary, stain all negative

immunohistochemistry slides with a

third antibody (i.e., patient negative

control) and the anti-HER2 staining

system.

3.4. Stain the positive and negative control

slides with the anti-HER2 staining

system and the appropriate antibodies .

4. Barcode all prepared slides and place

within open slots in the slide carriers.

5. Manually study the positive and negative

control slides and verify the staining is

in control. Manually study the patient's

positive and negative immunohistochemistry slides and verify patient doesn't have an

abnormality in background staining.

6. Work-list entry

6.1. Enter the information that is

consistent per work-list.

6.1.1. Specify the work-list

identification.

6.1.2. Specify the

protocol/application (HER2/neu)

6.1.3. Specify the barcodes of the

positive and negative control

slides

6.2. Enter the information that is related

to each patient case.

6.2.1. Accession number and Patient

Identification (ID)

6.2.2. Specify the two/three barcodes

of the samples for this case (H/E,

immunohistochemistry, patient

negative control) 6.2.3. Patient specific information:

Age (integer), Sex (M/F),Race

(character - predefined

selections) , Diagnosis (character

- 2S6) , Date of diagnosis (Date

Format) , Date of last treatment

(Date Format) , and Description of

last treatment (character - 512) .

7. If daily calibration of the instrument

hasn't been completed, please do so before

starting the work-list (5 min per

calibration, necessary once per a day) .

This calibration consists of radiometric,

geometric, and color standard calibration.

8. Load the carriers containing the control

slides and patient cases into the input

hopper .

9. Start the automated analysis of the work-

list.

9.1. Should the stain controls not have a

large enough delta Δ, the instrument skips the rest of the slides within the

worklist .

10. After completion of the analysis, review

the results of the work-list and construct

the appropriate reports online. For each

case, the low power H/E image appears

automatically with the selection of the

patient ID, accession number, or sample

identification .

10.1. While viewing the low power H/E

Histological Reconstruction (HR) the

user has the following choices:

10.1.1. Select an area of the H/E HR

to zoom on .

10.1.2. Choose to go to the low power

immunohistochemistry HR.

10.1.3. Select an area to save within

the pre-constructed report format.

10.2. While viewing the high power H/E

HR the user has the following choices : 10.2.1. Choose to go back to the low

power H/E HR.

10.3. While viewing the low power

immunohistochemistry HR the user has

the following choices :

10.3.1. Select an area of the

immunohistochemistry HR to zoom

on.

10.3.2. Select an area of the low

power immunohistochemistry HR and

query for the following

quantitative results . The

quantitative results are relative

to the scoring of the positive

stain control and the patient's

negative immunohistochemistry

slide.

10.3.2.1. Over-expression within

selected area reported as

0, 1+, 2+, or 3+. 10.3.2.2. Percentage of positive

cells within the selected

area.

10.4. While viewing the high power

immunohistochemistry HR the user has

the following choices :

10.4.1. Select an area of the high

power immunohistochemistry HR and

query for the following

quantitative results . The

quantitative results are relative

to the scoring of the positive

stain control and the patient's

negative immunohistochemistry

slide.

10.4.1.1. Over-expression within

selected area reported as

0, 1+, 2+, or 3+.

10.4.1.2. Percentage of positive

cells within the selected

area. 10.4.2. Choose to go back to the low

power immunohistochemistry HR.

10.5. Other samples to review?

10.5.1. If yes, repeat section 8.

10.6. Should the analysis result in the

tissue not being scanned, reload slide,

open application, and adjust scan area

to location of the tissue, if

necessary. Should the scan area be

adequate, but the focus points not fall

on an area of tissue, resulting in

failed focus, in the application change

the focus inset to a number closer to

one and confirm that the scan area is

centered on an area of tissue. Execute

the application. Repeat steps 10.1-

10.5.

10.7. Manually record results of

analysis .

11. Quality Assurance checks within the

instrument . 11.1. At the completion of analyzing

both the positive and negative control

slides, the Δ between the scores are

calculated. If this Δ isn't large

enough, the analysis of all slides in

this work-list is flagged accordingly

and the instrument warns the user with

an audible alarm.

11.2. If the Δ between the patient's

positive and negative

immunohistochemistry slide isn't large

enough, the patient is flagged

accordingly.

11.3. A montage of tissue sections from

both the positive and negative

controls. Five tissue section images

from each are adequate .

12. Preview the reports online and modify as

necessary.

13. Send the reports to the printer or by the

Internet to the requesting clinicians. The output of the worklist is a stained

positive control slide, a stained negative

control slide, two stained slides per

patient case (one H/E and another IHC) , and

a comprehensive electronic report. The

report includes the selected image

sections, quantitative results, reference

material, and other pertinent data.

Sample Staining

A cellular specimen (a "sample") is split to

provide two or more subsamples. The term "sample"

includes cellular material derived from a subject.

Such samples include but are not limited to hair,

skin samples, tissue sample, cultured cells, cultured

cell media, and biological fluids. The term

"tissue" refers to a mass of connected cells (e.g.,

CNS tissue, neural tissue, or eye tissue) derived

from a human or other animal and includes the

connecting material and the liquid material in

association with the cells. The term "biological fluid" refers to liquid material derived from a

human or other animal . Such biological fluids

include, but are not limited to, blood, plasma,

serum, serum derivatives, bile, phlegm, saliva,

sweat, amniotic fluid, and cerebrospinal fluid (CSF) ,

such as lumbar or ventricular CSF. The term

"sample" also includes media containing isolated

cells . The quantity of sample required to obtain a

reaction may be determined by one skilled in the art

by standard laboratory techniques. The optimal

quantity of sample may be determined by serial

dilution.

The HER2/neu method uses an anti-HER2/neu

staining system, such as a commercially available

kit, like that provided by DAKO (Carpinteria, CA) .

The steps for the immunohistochemistry protocol are

as follows: (1) Prepare wash buffer solution.

(2) Deparaffinize and rehydrate specimens. (3)

Perform epitope retrieval. Incubate 40 min in a 95°C

water bath. Cool slides for 20 min at room

temperature. (4) Apply peroxidase blocking reagent. Incubate 5 min. (5) Apply primary antibody or

negative control reagent. Incubate 30 min +/- 1 min

at room temperature. Rinse in wash solution. Place

in wash solution bath. (6) Apply peroxidase labeled

polymer. Incubate 30 min +/- 1 min at room

temperature. Rinse in wash solution. Place in wash

solution bath. (7) Prepare DAB substrate chromagen

solution. (8) Apply substrate chromogen solution

(DAB) . Incubate 5-10 min. Rinse with distilled

water; (9) Counterstain. (10) Mount coverslips. The

slide includes a cover-slip medium to protect the

sample and to introduce optical correction consistent

with microscope objective requirements. The

coverslip covers the entire prepared specimen.

Mounting the coverslip does not introduce air bubbles

obscuring the stained specimen. This coverslip could

potentially be a mounted 1-1/2 thickness coverslip

with DAKO Ultramount medium. (11) A set of staining

control slides are run with every worklist. The set

includes a positive and negative control. The

positive control is stained with the anti-HER2 antibody and the negative is stained with another

antibody. Both slides are identified with a unique

barcode. Upon reading the barcode, the instrument

recognizes the slide as part of a control set, and

runs the appropriate application. There may be one

or two applications for the stain controls. (12) A

set of instrument calibration slides includes the

slides used for focus and color balance calibration.

(13) A dedicated carrier is used for one-touch

calibration. Upon successful completion of this

calibration procedure, the instrument reports itself

to be calibrated. Upon successful completion of

running the standard slides, the user is able to

determine whether the instrument is within standards

and whether the inter-instrument and intra-instrument

repeatability of test results.

The hematoxylin/eosin (H/E) slides are prepared

with a standard H/E protocol . Standard solutions

include the following: (1) Gills hematoxylin

(hematoxylin 6.0 g; aluminium sulphate 4.2 g; citric

acid 1.4 g; sodium iodate 0.6 g; ethylene glycol 269 ml; distilled water 680 ml) ; (2) eosin (eosin

yellowish 1.0 g; distilled water 100 ml); (3) lithium

carbonate 1% (lithium carbonate 1 g; distilled water

100 g) ; (4) acid alcohol 1% 70% (alcohol 99 ml cone;

hydrochloric acid 1 ml); and (5) Scott's tap water.

In a beaker containing 1 L distilled water, add 20 g

sodium bicarbonate and 3.5 g magnesium sulphate. Add

a magnetic stirrer and mix thoroughly to dissolve the

salts. Using a filter funnel, pour the solution into

a labeled bottle.

The staining procedure is as follows: (1) Bring

the sections to water; (2) place sections in

hematoxylin for 5 min; (3) wash in tap water; (4)

'blue' the sections in lithium carbonate or Scott's

tap water; (5) wash in tap water; (6) place sections

in 1% acid alcohol for a few seconds; (7) wash in tap

water; (8) place sections in eosin for 5 min; (9) wash

in tap water; and (10) dehydrate, clear. Mount

sections .

The results of the H/E staining provide cells

with nuclei stained blue-black, cytoplasm stained varying shades of pink; muscle fibers stained deep

pinky red; fibrin stained deep pink; and red blood

cells stained orange-red.

The invention also provides automated methods

for analysis of estrogen receptor and progesterone

receptor. The estrogen and progesterone receptors,

like other steroid hormone receptors, plays a role in

developmental processes and maintenance of hormone

responsiveness in target cells. From the molecular

viewpoint, estrogen and progesterone receptor

interaction with target genes is of paramount

importance in maintenance of normal cell function and

is also involved in regulation of mammary tumor cell

function. The expression of progesterone receptor

and estrogen receptor in breast tumors is a useful

indicator for subsequent hormone therapy. An anti-

estrogen receptor antibody labels epithelial cells of

breast carcinomas which express estrogen receptor.

An immunohistochemical assay of the estrogen receptor

is performed using an anti-estrogen receptor

antibody, for example the well-characterized 1D5 clone, and the methods of Pertchuk, et al . (Cancer

77: 2514-2519, 1996) or a commercially available

immunohistochemistry system such as that provided by

DAKO (Carpenteria CA; DAKO LSAB2 Immunostaining

System) .

In breast carcinoma cells, immunohistochemistry

immunostaining of progesterone receptor has been

demonstrated in the nuclei of cells from various

histologic subtypes. An anti-progesterone receptor

antibody labels epithelial cells of breast carcinomas

which express progesterone receptor. An

immunohistochemical assay of the progesterone

receptor is performed using an anti-estrogen receptor

antibody, for example the well-characterized 1A6

clone, and the methods of Pertchuk, et al . (Cancer

77: 2514-2519, 1996) .

Still other automated analyses are within the

scope of the invention, including the following:

Micrometastases/Metastatic Recurring Disease

(MM/MRD) . Metastasis is the biological process

whereby a cancer spreads to the distant part of the body from its original site. A micrometastases is

the presence of a small number of tumor cells,

particularly in the lymph nodes and bone marrow. A

metastatic recurring disease is similar to

micrometastasis, but is detected after cancer therapy

rather than before therapy. An immunohistochemical

assay for MM/MRD is performed using a monoclonal

antibody which reacts with an antigen (a metastatic-

specific mucin) found in bladder, prostate and breast

cancers .

MIB-1. MIB-1 is an antibody that can be used in

immunohistochemical assays for the antigen Ki-67.

The clinical stage at first presentation is related

to the proliferative index measured with Ki-67. High

index values of Ki-67 are positively correlated with

metastasis, death from neoplasia, low disease-free

survival rates, and low overall survival rates.

Microvessel Density Analysis. Microvessel

density analysis is a measure of new blood vessel

formation (angiogenesis) . Angiogenesis is

characteristic of growing tumors . Intratumor microvessel density can be assessed by anti-CD34

immunostaining .

The Oncogene p53. Overexpression of the p53

oncogene has been implicated as the most common

genetic alteration in the development of human

malignancies . Investigations of a variety of

malignancies, including neoplasms of breast, colon,

ovary, lung, liver, mesenchyme, bladder and myeloid,

have suggested a contributing role of p53 mutation in

the development of malignancy. The highest frequency

of expression has been demonstrated in tumors of the

breast, colon and ovary. A wide variety of normal

cells do express a wildtype form of p53 but generally

in restricted amounts . Overexpression and mutation

of p53 have not been recognized in benign tumors or

in normal tissue. An immunohistochemical assay of

p53 is performed using an anti-p53 antibody, for

example the well-characterized DO-7 clone.

Immunophenotyping .

Undifferentiated tumor classification.

Antibody titering.

Prominence of Nucleoli. A nucleoli is an

organelle in a cell nucleus. Cervical dysplasia

refers to the replacement of the normal or

metaplastic epithelium with atypical epithelial cells

that have cytologic features that are pre-malignant

(nuclear hyperchromatism, nuclear enlargement and

irregular outlines, increased nuclear-to-cytoplasmic

ratio, increased prominence of nucleoli) and

chromosomal abnormalities . The changes seen in

dysplastic cells are of the same kind but of a lesser

degree than those of frankly malignant cells. In

addition, there are degrees of dysplasia (mild,

moderate, severe) .

HIV p24 Protein. Human immunodeficiency virus

(HIV) p24 antigen levels are measured in an

immunohistochemistry assay using anti-HIV p24

antigen. The HIV p24 protein test is used for the

detection of HIV virus. The test can be used to detect the virus, measure the amount of virus, and

examine the virus' genetic composition.

Human Papiloma Virus (HPV) . HPV is a sexually

transmitted virus which causes warts . HPV is an

important health concern because it may cause cancer

of the cervix. An immunohistochemical assay of HPV

is performed using an antibody associated with

cervical cancer. Several serological responses are

strongly associated with cervical cancer, notably the

IgG response against the HPV 16 epitopes LI: 13,

E2:9, and E7 : 5 , and the IgA response against an HPV

18 E2-derived antigen.

Mitotic Index. When a viral genome is

incorporated into the nuclear genetic material,

uncontrolled malignant growth of the host cell may be

promoted.

Operating the Instrument

While operating the instrument, the user

interacts with a few screens including, but not

limited to, Patient Entry, Review Data, Construct Report, Manual Control, and User Preferences. Rather

than the information entry revolving around the

patient cases, the patient data entry interface is

organized as worklist information composed of

multiple patient cases. For example, a worklist for

HER2/neu is composed of tissue samples that are

prepared in one of two methods, the HER2/neu

immunohistochemistry staining technique or the H/E

technique. The slides within a HER2/neu worklist

consists of a positive control slide (prepared with

the immunohistochemistry technique and the HER2

antibody) , a negative control slide (prepared with

the immunohistochemistry technique and another

antibody) , and two slides for the one or more patient

cases. Per patient case there are two slides

prepared. One slide is prepared with the HER2/neu

immunohistochemistry technique and a second is

prepared with the H/E technique for general tissue

analysis .

The data that is relevant for a HER2/neu

worklist is the: (1) worklist name or identifier of protocol (e.g., HER2/neu) ; barcode of the positive

control slide; and barcode of the negative control

slide. The data that is relevant on a per Patient

Case is the: (4) patient ID; (5) accession number;

(6) social security number; (7) referring hospital,

(8) referring physician; (9) barcode of the

immunohistochemistry prepared slide; and (10) barcode

of the H/E prepared slide.

The user starts analysis with the batch button

and the slide carriers loaded into the input hopper.

The input hopper is composed of all the

immunohistochemistry slides from the patient cases,

all the H/E slides from the patient cases, the

positive control slide, and the negative control

slide. During analysis the instrument reads the

barcode on the slide and determines one of three

situations and starts one of the three appropriate

computer applications: (1) Slide is the positive

control slide and run the HER2-positive application.

(2) Slide is the negative control slide and run the

HER2-negative application. (3) Slide is either the immunohistochemistry or H/E patient slide and run the

HER2 application.

The system reads industry standard barcodes .

The barcode relates the type of protocol the

instrument uses to analyze the slide. The type of

data stored for each slide is determined by the stain

preparation and the protocol used to analyze it .

During the transport of the slide through the system,

therefore, the chain of custody is preserved. When a

barcode is unreadable or isn't found in one of the

defined worklists, the action taken by the instrument

is to ignore the slide altogether and send it to the

output hopper .

The HER2-positive application (1) finds the

specimen, (2) goes to five locations within the

specimen, (3) stores the image from each location,

(4) completes color space transformations to obtain

the intensity of stain at all five locations, and (5)

stores the average value of intensity in the database

(DB) . This value is used as the normalized

expression value for the HER2 quantitative analysis. This value is also used to determine the Δ between

the positive and negative control slides and verify

that the Δ is large enough.

The HER2-negative application (1) finds the

specimen, (2) goes to five locations within the

specimen, (3) stores the image from each location,

(4) completes color space transformations to obtain

the intensity of stain at all five locations, and (5)

stores the average value of intensity in the

database. This value is used to determine the Δ

between the positive and negative control slides and

verify that the Δ is large enough.

The HER2 application (1) finds the specimen, (2)

scans the slide at lOx and constructs a histological

reconstruction object, and (3) stores the object in

the database. Before a slide is analyzed, the

instrument checks to see if the hard drive space has

met a certain capacity for archiving data. If so,

the user is prompted to archive before continuing

instrument analysis. Compression may need to take place prior to the object being stored within the

database.

After completion of running all the slides

within the worklist, the histological reconstruction

objects are analyzed by the pathologist or physician.

The Slide Display Interface displays the list of

slides that were analyzed, or the list of patient

cases. Within tissue analysis, there are always at

least two slides per patient case that are analyzed

by the instrument. The set of patient slides is a

logical unit. Therefore, a pathologist or physician

may choose the patient case by the patient ID or the

accession number.

Within the User Interface that displays the list

of patient cases to be reviewed/analyzed, once the

pathologist chooses the patient case, H/E image is

always displayed first. Using the H/E image, the

pathologist determines whether or not the cancer is

invasive and whether or not analysis of the IHC slide

is necessary. Upon patient case selection, the low power H/E

image is displayed. This image can be reduced to

enable the whole tissue cross-section to be displayed

within the system's monitor (resolution at least 1024

x 760) .

The pathologist can have the features available

to choose more than one patient case to review and

then traverse from one case to the next with the

following features. For each patient case, the

reduced low power H/E image always appears first .

While viewing the reduced low power H/E image

the pathologist has the following capabilities: (1)

to zoom into an area of the low power H/E image for

higher magnification; (2) to choose to go to the

reduced low power immunohistochemistry image for the

current patient case; (3) to select an area to

incorporate within the patient report (Marking an

image section) ; (4) to choose to go to the next

patient case (if selected in previous user

interface) ; and (5) to choose to go to the previous

patient case. While viewing the zoomed H/E image the

pathologist has the following capabilities: (1) to

go back to the reduced low power H/E image; (2) to

choose to go to the reduced low power

immunohistochemistry image for the current patient

case; (3) to select an area to incorporate within the

patient report (Marking an image section) ; (4) to

choose to go to the next patient case (if selected in

previous user interface) ; and (5) to choose to go to

the previous patient case.

While viewing the reduced low power

immunohistochemistry image the pathologist has the

following capabilities: (1) to zoom into an area of

the low power immunohistochemistry image for higher

magnification; (2) to choose to go back to the

reduced H/E image for the current patient case; (3)

to select an area of the reduced immunohistochemistry

image and query for quantitative results; (4) to

select an area to incorporate within the patient

report (Marking an image section) ; (5) to select a

quantitative result to incorporate within the patient report (Save the quantitative result) ; (6) to choose

to go to the next patient case (if selected in

previous user interface) ; and (7) to choose to go to

the previous patient case (if applicable) .

While viewing the zoomed immunohistochemistry

image the pathologist has the following capabilities :

(1) to go back to the reduced low power IHC image;

(2) to choose to go back to the reduced H/E image for

the current patient case; (3) to select an area of

the zoomed immunohistochemistry image and query for

quantitative results; (4) to select an area to

incorporate within the patient report (Marking an

image section) ; (5) to select a quantitative result

to incorporate within the patient report (Save the

quantitative result) ; (6) to choose to go to the next

patient case (if selected in previous user

interface) ; and (7) to choose to go to the previous

patient case (if applicable) .

After completion of the pathologist analysis and

review of all the patient cases, the reports can be

generated. A User Interface lists all the patient cases that have already been reviewed and analyzed by

the pathologist. The pathologist can choose one or

more of the listed patient cases to view the reports

electronically. The HER2/neu report for a patient

case can contain the following information: (1)

Patient Information and Demographics as entered

within the Worklist Information Entry; (2) Referring

Physician and Hospital as entered within the Worklist

Information Entry; (3) Pathologist and Laboratory

Information; (4) Sections of the H/E image marked

during the review/analysis for report inclusion; (5)

sections of the immunohistochemistry image marked

during the review/analysis for report inclusion;

(5) quantitative analysis saved during the

review/analysis for report inclusion; and (6)

HER2/neu application and scoring analysis reference

materials .

The interface that displays the patient reports

online contains the following functionality: (1)

Print; (2) Send To, allows for (linked to e-mail,

when available, and prompting the user for the address of where to send the report) ; (3) Next

Patient Case Report (if more than one case was

selected from the previous interface) ; (4) Previous

Patient Case Report (if applicable) ; and

(5) Save/Apply (if the additional comments area is

modified). Network connectivity is site specific.

Site specificity is desired for sending reports via

the Internet without having to transport the report

over to another host to send it. Direct sending

capabilities are preferred.

The instrument can have at least two user modes .

One user mode is a locked protocol consistent with

FDA regulations . A second user mode is modifiable by

the user. For the second user mode, access to the

following parameters of the application include: (1)

definition of the scan area, center, width, and

height; (2) toggle switch for whether or not the Find

phase is used; (3) Focus Type; (4) Focus Threshold;

and (5) Focus Inset. Automated System

Referring now to the figures, an apparatus for

automated cell analysis of biological specimens is

generally indicated by reference numeral 10 as shown

in perspective view in Fig. 1 and in block diagram

form in Fig. 2. The apparatus 10 comprises a

microscope subsystem 32 housed in a housing 12. The

housing 12 includes a slide carrier input hopper 16

and a slide carrier output hopper 18. A door 14 in

the housing 12 secures the microscope subsystem from

the external environment . A computer subsystem

comprises a computer 22 having a system processor 23,

an image processor 25 and a communications modem 29.

The computer subsystem further includes a computer

monitor 26 and an image monitor 27 and other external

peripherals including storage device 21, pointing

device 30, keyboard 28 and color printer 35. An

external power supply 24 is also shown for powering

the system. Viewing oculars 20 of the microscope

subsystem project from the housing 12 for operator

viewing. The apparatus 10 further includes a CCD camera 42 for acquiring images through the microscope

subsystem 32. A microscope controller 31 under the

control of system processor 23 controls a number of

microscope-subsystem functions described further in

detail . An automatic slide feed mechanism in

conjunction with X-Y stage 38 provide automatic slide

handling in the apparatus 10. An illumination light

source 48 projects light onto the X-Y stage 38 which

is subsequently imaged through the microscope

subsystem 32 and acquired through CCD camera 42 for

processing in the image processor 25. A Z stage or

focus stage 46 under control of the microscope

controller 31 provides displacement of the microscope

subsystem in the Z plane for focusing. The

microscope subsystem 32 further includes a motorized

objective turret 44 for selection of objectives.

The purpose of the apparatus 10 is for the

unattended automatic scanning of prepared microscope

slides for the detection and counting of candidate

objects of interest such as normal and abnormal

cells, e.g., tumor cells. The preferred embodiment may be utilized for rare event detection in which

there may be only one candidate object of interest

per several hundred thousand normal cells, e.g., one

to five candidate objects of interest per 2 square

centimeter area of the slide. The apparatus 10

automatically locates and counts candidate objects of

interest and estimates normal cells present in a

biological specimen on the basis of color, size and

shape characteristics . A number of stains are used

to preferentially stain candidate objects of interest

and normal cells different colors so that such cells

can be distinguished from each other.

As noted in the background of the invention, a

biological specimen may be prepared with a reagent to

obtain a colored insoluble precipitate. The

apparatus of the present invention is used to detect

this precipitate as a candidate object of interest.

During operation of the apparatus 10, a pathologist

or laboratory technician mounts prepared slides onto

slide carriers. A slide carrier 60 is illustrated in

Fig. 8 and will be described further below. Each slide carrier holds up to 4 slides. Up to 25 slide

carriers are then loaded into input hopper 16. The

operator can specify the size, shape and location of

the area to be scanned or alternatively, the system

can automatically locate this area. The operator

then commands the system to begin automated scanning

of the slides through a graphical user 25 interf ce.

Unattended scanning begins with the automatic loading

of the first carrier and slide onto the precision

motorized X-Y stage 38. A bar code label affixed to

the slide is read by a bar code reader 33 during this

loading operation. Each slide is then scanned at a

user selected low microscope magnification, for

example, lOx, to identify candidate objects of

interest based on their color, size, and shape

characteristics. The X-Y locations of candidate

cells are stored until scanning is completed.

After the low magnification scanning is

completed, the apparatus automatically returns to

each candidate object of interest, reimages and

refocuses at a higher magnification such as 40x and performs further analysis to confirm the object

candidate . The apparatus stores an image of the

object for later review by a pathologist. All

results and images can be stored to a storage device

21 such as a removable hard drive or DAT tape or

transmitted to a remote site for review or storage.

The stored images for each slide can be viewed in a

mosaic of images for further review. In addition,

the pathologist or operator can also directly view a

detected object through the microscope using the

included oculars 20 or on image monitor 27.

Having described the overall operation of the

apparatus 10 from a high level, the further details

of the apparatus will now be described. Referring to

Fig. 3, the microscope controller 31 is shown in more

detail. The microscope controller 31 includes a

number of subsystems connected through a system bus .

A system processor 102 controls these subsystems and

is controlled by the apparatus system processor 23

through an RS 232 controller 110. The system

processor 102 controls a set of motor - control subsystems 114 through 124 which control the input

and output feeder, the motorized turret 44, the X-Y

stage 38, and the Z stage 46 (Fig. 2) . A histogram

processor 108 receives input from CCD camera 42 for

computing variance data during the focusing operation

described further herein. The system processor 102

further controls an illumination controller 106 for

control of substage illumination 48. The light

output from the halogen light bulb which supplies

illumination for the system can vary over time due to

bulb aging, changes in optical alignment, and other

factors. In addition, slides which have been "over

stained" can reduce the camera exposure to an

unacceptable level . In order to compensate for these

effects, the illumination controller 106 is included.

This controller is used in conjunction with light

control software to compensate for the variations in

light level . The light control software samples the

output from the camera at intervals (such as between

loading of slide carriers) , and commands the

controller to adjust the light level to the desired levels . In this way, light control is automatic and

transparent to the user and adds no substantial

additional time to system operation. The system

processor 23 may be any processor capable of

performing the operations of the system, for example

dual parallel Intel Pentium 90 MHZ devices, a T.I.

C80 processor for computation and processors capable

of 8 to 32 bit operation on an NT system. The image

processor 25 is preferably a Matrox Imaging Series

640 model. The microscope controller system

processor 102 is an Advanced Micro Devices AMD29K

device .

Figs . 4 and 5 show further detail of the

apparatus 10 is shown. Fig. 4 shows a plan view of

the apparatus 10 with the housing 12 removed. A

portion of the automatic slide feed mechanism 37 is

shown to the left of the microscope subsystem 32 and

includes slide carrier unloading assembly 34 and

unloading platform 36 which in conjunction with slide

carrier output hopper 18 function to receive slide

carriers which have been analyzed. Vibration isolation mounts 40, shown in further detail in Fig.

5, are provided to isolate the microscope subsystem

32 from mechanical shock and vibration that can occur

in a typical laboratory environment. In addition to

external sources of vibration, the high-speed

operation of the X-Y stage 38 can induce vibration

into the microscope subsystem 32. Such sources of

vibration can be isolated from the electro-optical

subsystems to avoid any undesirable effects on image

quality. The isolation mounts 40 comprise a spring

40a and piston 40b submerged in a high viscosity

silicon gel which is enclosed in an elastomer

membrane bonded to a casing to achieve damping

factors on the order of 17 to 20%.

The automatic slide-handling feature of the

present invention will now be described. The

automated slide handling subsystem operates on a

single slide carrier at a time. A slide carrier 60

is shown in Figs . 6a and 6b which provide a top view

and a bottom view respectively. The slide carrier 60

includes up to four slides 70 mounted with adhesive tape 62. The carrier 60 includes ears 64 for hanging

the carrier in the output hopper 18. An undercut 66

and pitch rack 68 are formed at the top edge of the

slide carrier 60 for mechanical handling of the slide

carrier. A keyway cutout 65 is formed in one side of

the carrier 60 to facilitate carrier alignment. A

prepared slide 72 mounted on the slide carrier 60

includes a sample area 72a and a bar code label area

72b. Fig. 7a provides a top view of the slide

handling subsystem which comprises a slide input

module 15, a slide output module 17 and X-Y stage

drive belt 50. Fig. 7b provides a partial cross-

sectional view taken along line A-A of Fig. 7a. The

slide input module 15 comprises a slide carrier input

hopper 16, loading platform 52 and slide carrier

loading subassembly 54. The input hopper 16 receives

a series of slide carriers 60 (Figs. 6a and 6b) in a

stack on loading platform 52. A guide key 57

protrudes from a side of the input hopper 16 to which

the keyway cutout 65 (Fig. 6a) of the carrier is fit

to achieve proper 15 alignment. The input module 15 further includes a revolving indexing cam 56 and a

switch 90 mounted in the loading platform 52, the

operation of which is described further below. The

carrier loading subassembly 54 comprises an infeed

drive belt 59 driven by a motor 86. The infeed drive

belt 59 includes a pusher tab 58 for pushing the

slide carrier horizontally toward the 20 X-Y stage 38

when the belt is driven. A homing switch 95 senses

the pusher tab 58 during a revolution of the belt 59.

Referring specifically to Fig. 7a, the X-Y stage

38 is shown with x position and y position motors 96

and 97 respectively which are controlled by the

microscope controller 31 (Fig. 3) and are not

considered part of the slide handling subsystem. The

X-Y stage 38 further includes an aperture 55 for

allowing illumination to reach the slide carrier. A

switch 91 is mounted adjacent the aperture 55 for

sensing contact with the carrier and thereupon

activating a motor 87 to drive stage drive belt 50

(Fig. 7b) . The drive belt 50 is a double-sided timing belt having teeth for engaging pitch rack 68

of the carrier 60 (Fig. 6b) .

The slide output module 17 includes slide

carrier output hopper 18, unloading platform 36 and

slide carrier unloading subassembly 34. The

unloading subassembly 34 comprises a motor 89 for

rotating the unloading platform 36 about shaft 98

during an unloading operation described further

below. An outfeed gear 93 driven by motor 88

rotatably engages the pitch rack 68 of the carrier 60

(Fig. 6b) to transport the carrier to a rest position

against switch 92. A springloaded hold-down

mechanism holds the carrier in place on the unloading

platform 36.

The slide handling operation will now be

described. Referring to Fig. 8, a series of slide

carriers 60 are shown stacked in input hopper 16 with

the top edges 60a aligned. As the slide handling

operation begins, the indexing cam 56 driven by motor

85 advances one revolution to allow only one slide carrier to drop to the bottom of the hopper 16 and

onto the loading platform 52.

Figs. 8a-8d show the cam action in more detail.

The cam 56 includes a hub 56a to which are mounted

upper and lower leaves 56b and 56c respectively. The

leaves 56b, 56c are semicircular projections

oppositely positioned and spaced apart vertically.

In a first position shown in Fig. 8a, the upper leaf

56b supports the bottom carrier at the undercut

portion 66. At a 20 position of the cam 56 rotated

180°, shown in Fig. 8b, the upper leaf 56b no longer

supports the carrier and instead the carrier has

dropped slightly and is supported by the lower leaf

56c. Fig. 8c shows the position of the cam 56

rotated 270° wherein the upper leaf 56b has rotated

sufficiently to begin to engage the undercut 66 of

the next slide carrier while the opposite facing

lower leaf 56c still supports the bottom carrier.

After a full rotation of 360° as shown in Fig. 8d,the

lower leaf 56c has rotated opposite the carrier stack

and no longer supports the bottom carrier which now rests on the loading platform 52. At the same

position, the upper leaf 56b supports the next

carrier for repeating the cycle.

Referring again to Figs. 7a and 7b, when the

carrier drops to the loading platform 52, the contact

closes switch 90 which activates motors 86 and 87.

Motor 86 drives the infeed drive belt 59 until the

pusher tab 58 makes contact with the carrier and

pushes the carrier onto the X-Y stage drive belt 50.

The stage drive belt 50 advances the carrier until

contact is made with switch 91, the closing of which

begins the slide scanning process described further

herein. Upon completion of the scanning process, the

X-Y stage 38 moves to an unload position and motors

87 and 88 are activated to transport the carrier to

the unloading platform 36 using stage drive belt 50.

As noted, motor 88 drives outfeed gear 93 to engage

the carrier pitch rack 68 of the carrier 60 (Fig. 6b)

until switch 92 is contacted. Closing switch 92

activates motor 89 to rotate the unloading platform

36. The unloading operation is shown in more detail

in end views of the output module 17 (Figs. 9a-9d) .

In Fig. 9a, the unloading platform 36 is shown in a

horizontal position supporting a slide carrier 60.

The hold-down mechanism 94 secures the carrier 60 at

one end. Fig. 9b shows the output module 17 after

motor 89 has rotated the unloading platform 36 to a

vertical position, at which point the spring loaded

hold-down mechanism 94 releases the slide carrier 60

into the output hopper 18. The carrier 60 is

supported in the output hopper 18 by means of ears 64

(Figs. 6a and 6b) . Fig. 9c shows the unloading

platform 36 being rotated back towards the 20

horizontal position. As the platform 36 rotates

upward, it contacts the deposited carrier 60 and the

upward movement pushes the carrier toward the front

of the output hopper 18. Fig. 9d shows the unloading

platform 36 at its original horizontal position after

having output a series of slide carriers 60 to the

output hopper 18. Having described the overall system and the

automated slide handling feature, the aspects of the

apparatus 10 relating to scanning, focusing and image

processing will now be described in further detail.

In some cases, an operator will know ahead of

time where the scan area of interest is on the slide.

Conventional preparation of slides for examination

provides repeatable and known placement of the sample

on the slide. The operator can therefore instruct

the system to always scan the same area at the same

location of every slide which is prepared in this

fashion. But there are other times in which the area

of interest is not known, for example, where slides

are prepared manually with a known smear technique.

One feature of the invention automatically determines

the scan area using a texture analysis process. Fig.

10 is a flow diagram that describes the processing

associated with the automatic location of a scan

area. As shown in this figure, the basic method is

to pre-scan the entire slide area to determine

texture features that indicate the presence of a smear and to discriminate these areas from dirt and

other artifacts.

At each location of this raster scan, an image

such as in Fig. 12 is acquired and analyzed for

texture information at steps 204 and 206. Since it

is desired to locate the edges of the smear sample

within a given image, texture analyses are conducted

over areas called windows 78, which are smaller than

the entire image as shown in Fig. 12. The process

iterates the scan across the slide at steps

208,210,212 and 214.

In the interest of speed, the texture analysis

process is performed at a lower magnification,

preferably at a 4x objective. One reason to operate

at low magnification is to image the largest slide

area at any one time. Since cells do not yet need to

be resolved at this stage of the overall image

analysis, the 4x magnification is preferred. On a

typical slide, as shown in Fig. 11, a portion 72b of

the end of the slide 72 is reserved for labeling with

identification information. Excepting this label area, the entire slide is scanned in a raster scan

fashion 76 to yield a number of adjacent images.

Texture values for each window include the pixel

variance over a window, the difference between the

largest and smallest pixel value within a window, and

other indicators . The presence of a smear raises the

texture values compared with a blank area.

One problem with a smear from the standpoint of

determining its location is its non-uniform thickness

and texture. For example, the smear is likely to be

relatively thin at the edges and thicker towards the

middle due to the nature of the smearing process . To

accommodate for the non-uniformity, texture analysis

provides a texture value for each analyzed area . The

texture value tends to gradually rise as the scan

proceeds across a smear from a thin area to a thick

area, reaches a peak, and then falls off again to a

lower value as a thin area at the edge is reached.

The problem is then to decide from the series of

texture values the beginning and ending, or the

edges, of the smear. The texture values are fit to a square wave waveform since the texture data does not

have sharp beginnings and endings .

After conducting this scanning and texture

evaluation operation, one must determine which areas

of elevated texture values represent the desired

smear 74, and which represent undesired artifacts.

This is accomplished by fitting a step function, on a

line-by-line basis to the texture values in step 216.

This function, which resembles a single square wave

across the smear with a beginning at one edge, and

end at the other edge, and an amplitude provides the

means for discrimination. The amplitude of the best-

fit step function is utilized to determine whether

smear or dirt is present since relatively high values

indicate smear. If it is decided that smear is

present, the beginning and ending coordinates of this

pattern are noted until all lines have been

processed, and the smear sample area defined at 218.

After an initial focusing operation described

further herein, the scan area of interest is scanned

to acquire images for image analysis . The preferred method of operation is to initially perform a

complete scan of the slide at low magnification to

identify and locate candidate objects of interest,

followed by further image analysis of the candidate

objects of interest at high magnification in order to

confirm the objects as cells. An alternate method of

operation is to perform high magnification image

analysis of each candidate object of interest

immediately after the object has been identified at

low magnification. The low magnification scanning

then resumes, searching for additional candidate

objects of interest. Since it takes on the order of

a few seconds to change objectives, this alternate

method of operation would take longer to complete .

The operator can pre-select a magnification

level to be used for the scanning operation. A low

magnification using a lOx objective is preferred for

the scanning operation since a larger area can be

initially analyzed for each acquired scan image. The

overall detection process for a cell includes a

combination of decisions made at both low (lOx) and high magnification (40x) levels. Decision making at

the lOx magnification level is broader in scope,

i.e., objects that loosely fit the relevant color,

size and shape characteristics are identified at the

lOx level.

Analysis at the 40x magnification level then

proceeds to refine the decision-making and confirm

objects as likely cells or candidate objects of

interest. For example, at the 40x level it is not

uncommon to find that some objects that were

identified at lOx are artifacts which the analysis

process will then reject. In addition, closely

packed objects of interest appearing at lOx are

separated at the 40x level.

In a situation where a cell straddles or

overlaps adjacent image fields, image analysis of the

individual adjacent image fields could result in the

cell being rejected or undetected. To avoid missing

such cells, the scanning operation compensates by

overlapping adjacent image fields in both the x and y

directions . An overlap amount greater than half the diameter of an average cell is preferred. In the

preferred embodiment, the overlap is specified as a

percentage of the image field in the x and y

directions .

The time to complete an image analysis can vary

depending upon the size of the scan area and the

number of candidate cells, or objects of interest

identified. For one example, in the preferred

embodiment, a complete image analysis of a scan area

of two square centimeters in which 50 objects of

interest are confirmed can be performed in about 12

to 15 minutes . This example includes not only

focusing, scanning, and image analysis, but also the

saving of 4Ox images as a mosaic on hard drive 21

(Fig. 2) . Consider the utility of the present

invention in a "rare event" application where there

may be one, two or a very small number of cells of

interest located somewhere on the slide. To

illustrate the nature of the problem by analogy, if

one were to scale a slide to the size of a football

field, a tumor cell, for example, would be about the size of a bottle cap. The problem is then to rapidly

search the football field and find the very small

number of bottle caps and have a high certainty that

none have been missed.

However the scan area is defined, an initial

focusing operation must be performed on each slide

prior to scanning. This is required since slides

differ, in general, in their placement in a carrier.

These differences include slight (but significant)

variations of tilt of the slide in its carrier.

Since each slide must remain in focus during

scanning, the degree of tilt of each slide must be

determined. This is accomplished with an initial

focusing operation that determines the exact degree

of tilt, so that focus can be maintained

automatically during scanning.

The initial focusing operation and other

focusing operations to be described later utilize a

focusing method based on processing of images

acquired by the system. This method was chosen for

its simplicity over other methods including use of IR beams reflected from the slide surface and use of

mechanical gauges . These other methods also would

not function properly when the specimen is protected

with a coverglass . The preferred method results in

lower system cost and improved reliability since no

additional parts need be included to perform

focusing. Fig. 13A provides a flow diagram

describing the "focus point" procedure. The basic

method relies on the fact that the pixel value

variance (or standard deviation) taken about the

pixel value mean is maximum at best focus . A

"brute-force" method could simply step through

focus, using the computer controlled Z or focus

stage, calculate the pixel variance at each step, and

return to the focus position providing the maximum

variance. Such a method would be too time consuming.

Therefore, additional features were added as shown in

Fig. 13A.

These features include the determination of

pixel variance at a relatively coarse number of focal

positions, and then the fitting of a curve to the data to provide a faster means of determining optimal

focus. This basic process is applied in two steps,

coarse and fine. During the coarse step at 220-230,

the Z stage is stepped over a user-specified range of

focus positions, with step sizes that are also user-

specified. It has been found that for coarse

focusing, these data are a close fit to a Gaussian

function. Therefore, this initial set of variance

versus focus position data are least-squares fit to a

Gaussian function at 228. The location of the peak

of this Gaussian curve determines the initial or

coarse estimate of focus position for input to step

232.

Following this, a second stepping operation 232-

242 is performed utilizing smaller steps over a

smaller focus range centered on the coarse focus

position. Experience indicates that data taken over

this smaller range are generally best fit by a second

order polynomial. Once this least squares fit is

performed at 240, the peak of the second order curve

provides the fine focus position at 244. Fig. 14 illustrates a procedure for how this

focusing method is utilized to determine the

orientation of a slide in its carrier. As shown,

focus positions are determined, as described above,

for a 3 x 3 grid of points centered on the scan area

at 264. Should one or more of these points lie

outside the scan area, the method senses at 266 this

by virtue of low values of pixel variance. In this

case, additional points are selected closer to the

center of the scan area. Fig. 15 shows the initial

array of points 80 and new point 82 selected closer

to the center. Once this array of focus positions is

determined at 268, a least squares plane is fit to

this data at 270. Focus points lying too far above

or below this best-fit plane are discarded at 272

(such as can occur from a dirty cover glass over the

scan area) , and the data is then refit . This plane

at 274 then provides the desired Z position

information for maintaining focus during scanning.

After determination of the best-fit focus plane,

the scan area is scanned in an X raster scan over the scan area as described earlier. During scanning, the

X stage is positioned to the starting point of the

scan area, the focus (Z) stage is positioned to the

best fit focus plane, an image is acquired and

processed as described herein, and this process is

repeated for all points over the scan area. In this

way, focus is maintained automatically without the

need for time-consuming refocusing at points during

scanning. Prior to confirmation of cell objects at a

40x or 60x level, a refocusing operation is conducted

since the use of this higher magnification requires

more precise focus than the best-fit plane provides.

Fig. 16 provides the flow diagram for this process.

As may be seen, this process is similar to the fine

focus method described earlier in that the object is

to maximize the image pixel variance. This is

accomplished by stepping through a range of focus

positions with the Z stage at 276, 278, calculating

the image variance at each position at 278, fitting a

second order polynomial to these data at 282, and

calculating the peak of this curve to yield an estimate of the best focus position at 284, 286.

This final focusing step differs from previous ones

in that the focus range and focus step sizes are

smaller since this magnification requires focus

settings to within 0.5 micron or better. It should

be noted that for some combinations of cell staining

characteristics, improved focus can be obtained by

numerically selecting the focus position that

provides the largest variance, as opposed to

selecting the peak of the polynomial. In such cases,

the polynomial is used to provide an estimate of best

focus, and a final step selects the actual Z position

giving highest pixel variance . It should also be

noted that if at any time during the focusing process

at 40x or 60x the parameters indicate that the focus

position is inadequate, the system automatically

reverts to a coarse focusing process as described

above with reference to Fig. 13A. This ensures that

variations in specimen thickness can be accommodated

in an expeditious manner. For some biological

specimens and stains, the focusing methods discussed above do not provide optimal focused results. For

example, certain white blood cells known as

neutrophils may be stained with Fast Red, a commonly

known stain, to identify alkaline phosphatase in the

cytoplasm of the cells . To further identify these

cells and the material within them, the specimen may

be counterstained with hemotoxylin to identify the

nucleus of the cells. In cells so treated, the

cytoplasm bearing alkaline phosphatase becomes a

shade of red proportionate to the amount of alkaline

phosphatase in the cytoplasm and the nucleus becomes

blue. However, where the cytoplasm and nucleus

overlap, the cell appears purple. These color

combinations appear to preclude the finding of a

focused Z position using the focus processes

discussed above.

In an effort to find a best focal position at

high magnification, a focus method, such as the one

shown in Fig. 13B, may be used. That method begins

by selecting a pixel near the center of a candidate

object of interest (Block 248) and defining a region of interest centered about the selected pixel (Block

250) . Preferably, the width of the region of

interest is a number of columns which is a power of

2. This width preference arises from subsequent

processing of the region of interest preferably using

a one dimensional Fast Fourier Transform (FFT)

technique. As is well known within, the art,

processing columns of pixel values using the FFT

technique is facilitated by making the number of

columns to be processed a power of two. While the

height of the region of interest is also a power of

two in the preferred embodiment, it need not be

unless a two dimensional FFT technique is used to

process the region of interest.

After the region of interest is selected, the

columns of pixel values are processed using the

preferred one-dimensional FFT to determine a spectra

of frequency components for the region of interest

(Block 252) . The frequency spectra ranges from DC to

some highest frequency component . For each frequency

component, a complex magnitude is computed. Preferably, the complex magnitudes for the frequency

components which range from approximately 25% of the

highest component to approximately 75% of the highest

component are squared and summed to determine the

total power for the region of interest (Block 254) .

Alternatively, the region of interest may be

processed with a smoothing window, such as a Hanning

window, to reduce the spurious high frequency

components generated by the FFT processing of the

pixel values in the region of interest . Such

preprocessing of the region of interest permits all

complex magnitude over the complete frequency range

to be squared and summed. After the power for a

region has been computed and stored (Block 256) , a

new focal position is selected, focus adjusted

(Blocks 258, 260) , and the process repeated. After

each focal position has been evaluated, the one

having the greatest power factor is selected as the

one best in focus (Block 262) .

The following describes the image processing

methods which are utilized to decide whether a candidate object of interest such as a stained tumor

cell is present in a given image, or field, during

the scanning process. Candidate objects of interest

which are detected during scanning are reimaged at

higher (40x or 60x) magnification, the decision

confirmed, and a region of interest for this cell

saved for later review by the pathologist . The image

processing includes color space conversion, low pass

filtering, background suppression, artifact

suppression, morphological processing, and blob

analysis. One or more of these steps can optionally

be eliminated. The operator is provided with an

option to configure the system to perform any or all

of these steps and whether to perform certain steps

more than once or several times in a row. It should

also be noted that the sequence of steps may be

varied and thereby optimized for specific reagents or

reagent combinations; however, the sequence described

herein is preferred. It should be noted that the

image processing steps of low pass filtering,

thresholding, morphological processing, and blob analysis are generally known image processing

building blocks.

An overview of the preferred process is shown in

Fig. 17A. The preferred process for identifying and

locating candidate objects of interest in a stained

biological specimen on a slide begins with an

acquisition of images obtained by scanning the slide

at low magnification (Block 288) . Each image is then

converted from a first color space to a second color

space (Block 290) and the color converted image is

low pass filtered (Block 292) . The pixels of the low

pass filtered image are then compared to a threshold

(Block 294) and, preferably, those pixels having a

value equal to or greater than the threshold are

identified as candidate object of interest pixels and

those less than the threshold are determined to be

artifact or background pixels. The candidate object

of interest pixels are then morphologically processed

to identify groups of candidate object of interest

pixels as candidate objects of interest (Block 296) .

These candidate objects of interest are then compared to blob analysis parameters (Block 298) to further

differentiate candidate objects of interest from

objects which do not conform to the blob analysis

parameters and, thus, do not warrant further

processing. The location of the candidate objects of

interest may be stored prior to confirmation at high

magnification. The process continues by determining

whether the candidate objects of interest have been

confirmed (Block 300) . If they have not been

confirmed, the optical system is set to high

magnification (Block 302) and images of the slide at

the locations corresponding to the candidate objects

of interest identified in the low magnification

images are acquired (Block 288) . These images are

then color converted (Block 290) , low pass filtered

(Block 292) , compared to a threshold (Block 294) ,

morphologically processed (Block 296) , and compared

to blob analysis parameters (Block 298) to confirm

which candidate objects of interest located from the

low magnification images are objects of interest. The coordinates of the objects of interest are then

stored for future reference (Block 303) .

In general, the candidate objects of interest,

such as tumor cells, are detected based on a

combination of characteristics, including size,

shape, and color. The chain of decision-making based

on these characteristics preferably begins with a

color space conversion process. The CCD camera

coupled to the microscope subsystem outputs a color

image comprising a matrix of 640 x 480 pixels. Each

pixel comprises red, green, and blue (ROB) signal

values .

It is desirable to transform the matrix of RGB

values to a different color space because the

difference between candidate objects of interest and

their background, such as tumor and normal cells, may

be determined from their respective colors.

Specimens are generally stained with one or more

industry standard stains (e.g., DAB, New Fuchsin,

AEC) which are "reddish" in color. Candidate

objects of interest retain more of the stain and thus appear red while normal cells remain unstained. The

specimens may also be counterstained with hematoxalin

so the nuclei of normal cells or cells not containing

an object of interest appear blue. In addition to

these objects, dirt and debris can appear as black,

gray, or can also be lightly stained red or blue

depending on the staining procedures utilized. The

residual plasma or other fluids also present on a

smear may also possess some color.

In the color conversion operation, a ratio of

two of the RGB signal values is formed to provide a

means for discriminating color information. With

three signal values for each pixel, nine different

ratios can be formed: R1R, R/G, RUB, G/G, G/B, G/R,

B/B, B/G, B/R. The optimal ratio to select depends

upon the range of color information expected in the

slide specimen. As noted above, typical stains used

for detecting candidate objects of interest such as

tumor cells are predominantly red, as opposed to

predominantly green or blue. Thus, the pixels of a

cell of interest which has been stained contain a red component which is larger than either the green or

blue components. A ratio of red divided by blue

(R/B) provides a value which is greater than one for

tumor cells but is approximately one for any clear or

white areas on the slide. Since the remaining cells,

i.e., normal cells, typically are stained blue, the

R/B ratio for pixels of these latter cells yields

values of less than one. The R/B ratio is preferred

for clearly separating the color information typical

in these applications .

Fig. 17B illustrates the flow diagram by which

this conversion is performed. In the interest of

processing speed, the conversion is implemented with

a look up table . The use of a look up table for

color conversion accomplishes three functions: (1)

performing a division operation; (2) scaling the

result for processing as an image having pixel values

ranging from 0 to 255; and (3) defining objects which

have low pixel values in each color band (R,G,B) as

"black" to avoid infinite ratios (i.e., dividing by

zero). These "black" objects are typically staining artifacts or can be edges of bubbles caused

by pasting a coverglass over the specimen.

Once the look up table is built at 304 for the

specific color ratio (i.e., choices of tumor and

nucleated cell stains) , each pixel in the original

RGB image is converted at 308 to produce the output.

Since it is of interest to separate the red stained

tumor cells from blue stained normal ones, the ratio

of color values is then scaled by a user specified

factor. As an example, for a factor of 128 and the

ratio of (red pixel value) / (blue pixel value), clear

areas on the slide would have a ratio of 1 scaled by

128 for a final X value of 128. Pixels which lie in

red stained tumor

cells would have X value greater than 128, while blue

stained nuclei of normal cells would have value less

than 128. In this way, the desired objects of

interest can be numerically discriminated. The

resulting 640 x 480 pixel matrix, referred to as the

X-image, is a gray scale image having values ranging

from 0 to 255. Other methods exist for discriminating color

information. One classical method converts the RGB

color information into another color space, such as

HSI (hue, saturation, intensity) space. In such a

space, distinctly different hues such as red, blue,

green, yellow, may be readily separated. In

addition, relatively lightly stained objects may be

distinguished from more intensely stained ones by

virtue of differing saturations. However, converting

from RGB space to HSI space requires more complex

computation. Conversion to a color ratio is faster;

for example, a full image can be converted by the

ratio technique of the present invention in about 30

ms while an HSI conversion can take several seconds.

In yet another approach, one could obtain color

information by taking a single color channel from the

camera. As an example, consider a blue channel, in

which objects that are red are relatively dark.

Objects which are blue, or white, are relatively

light in the blue channel. In principle, one could

take a single color channel, and simply set a threshold wherein everything darker than some

threshold is categorized as a candidate object of

interest, for example, a tumor cell, because it is

red and hence dark in the channel being reviewed.

However, one problem with the single channel approach

occurs where illumination is not uniform. Non-

uniformity of illumination results in non-uniformity

across the pixel values in any color channel, for

example, tending to peak in the middle of the image

and dropping off at the edges where the illumination

falls off. Performing thresholding on this non-

uniform color information runs into problems, as the

edges sometimes fall below the threshold, and

therefore it becomes more difficult to pick the

appropriate threshold level. However, with the ratio

technique, if the values of the red channel fall off

from center to edge, then the values of the blue

channel also fall off center to edge, resulting in a

uniform ratio, non-uniformities. Thus, the ratio

technique is more immune to illumination. As previously described, the color conversion

scheme is relatively insensitive to changes in color

balance, i.e., the relative outputs of the red,

green, and blue channels. However, some control is

necessary to avoid camera saturation, or inadequate

exposures in any one of the color bands. This color

balancing is performed automatically by utilizing a

calibration slide consisting of a clear area, and a

"dark" area having a known optical transmission or

density. The system obtains images from the clear

and "dark" areas, calculates "white" and "black"

adjustments for the image processor 25, and thereby

provides correct color balance.

In addition to the color balance control,

certain mechanical alignments are automated in this

process. The center point in the field of view for

the various microscope objectives as measured on the

slide can vary by several (or several tens of)

microns. This is the result of slight variations in

position of the microscope objectives 44a as

determined by the turret 44 (Fig. 4), small variations in alignment of the objectives with

respect to the system optical axis, and other

factors. Since it is desired that each microscope

objective be centered at the same point, these

mechanical offsets must be measured and automatically

compensated.

This is accomplished by imaging a test slide

which contains a recognizable feature or mark. An

image of this pattern is obtained by the system with

a given objective, and the position of the mark

determined. The system then rotates the turret to

the next lens objective, obtains an image of the test

object, and its position is redetermined. Apparent

changes in position of the test mark are recorded for

this objective. This process is continued for all

objectives. Once these spatial offsets have been

determined, they are automatically compensated for by

moving the stage 38 by an equal (but opposite) amount

of offset during changes in objective. In this way,

as different lens objectives are selected, there is

no apparent shift in center point or area viewed. A low pass filtering process precedes

thresholding. An objective of thresholding is to

obtain a pixel image matrix having only candidate

objects of interest, such as tumor cells above a

threshold level and everything else below it.

However, an actual acquired image will contain noise.

The noise can take several forms, including white

noise and artifacts. The microscope slide can have

small fragments of debris that pick up color in the

staining process and these are known as artifacts.

These artifacts are generally small and scattered

areas, on the order of a few pixels, which are above

the threshold. The purpose of low pass filtering is

to essentially blur or smear the entire color

converted image. The low pass filtering process will

smear artifacts more than larger objects of interest.

Such as tumor cells and thereby eliminate or reduce

the number of artifacts that pass the thresholding

process . The result is a cleaner thresholded image

downstream. In the low pass filter process, a 3 x 3

matrix of coefficients is applied to each pixel in the 640 x 480 x-image. A preferred coefficient

matrix is as follows:

1/9 1/9 1/9

1/9 1/9 1/9

1/9 1/9 1/9

At each pixel location, a 3 x 3 matrix comprising the

pixel of interest and its neighbors is multiplied by

the coefficient matrix and summed to yield a single

value for the pixel of interest . The output of this

spatial convolution process is again a 640 x 480

matrix. As an example, consider a case where the

center pixel and only the center pixel, has a value

of 255 and each of its other neighbors, top left,

top, top right and so forth, have values of 0.

This singular white pixel case corresponds to a

small object. The result of the matrix

multiplication and addition using the coefficient

matrix is a value of 1/9 (255) or 28 for the center

pixel, a value which is below the nominal threshold

of 128. Now consider another case in which all the

pixels have a value of 255 corresponding to a large object. Performing the low pass filtering operation

on a 3 x 3 matrix for this case yields a value of 255

for the center pixel. Thus, large objects retain

their values while small objects are reduced in

amplitude or eliminated. In the preferred method of

operation, the low pass filtering process is

performed on the X image twice in succession.

In order to separate objects of interest, such

as a tumor cell in the x image from other objects and

background, a thresholding operation is performed

designed to set pixels within cells of interest to a

value of 255, and all other areas to 0. Thresholding

ideally yields an image in which cells of interest

are white and the remainder of the image is black. A

problem one faces in thresholding is where to set the

threshold level. One cannot simply assume that cells

of interest are indicated by any pixel value above

the nominal threshold of 128. A typical imaging

system may use an incandescent halogen light bulb as

a light source. As the bulb ages, the relative

amounts of red and blue output can change. The tendency as the bulb ages is for the blue to drop off

more than the red and the green. To accommodate for

this light source variation over time, a dynamic

thresholding process is used whereby the threshold is

adjusted dynamically for each acquired image. Thus,

for each 640 x 480 image, a single threshold value is

derived specific to that image.

As shown in Fig. 18, the basic method is to

calculate, for each field, the mean X value, and the

standard deviation about this mean at 312. The

threshold is then set at 314 to the mean plus an

amount defined by the product of a (user specified)

factor and the standard deviation of the color

converted pixel values. The standard deviation

correlates to the structure and number of objects in

the image. Preferably, the user specified factor is

in the range of approximately 1.5 to 2.5. The factor

is selected to be in the lower end of the range for

slides in which the stain has primarily remained

within cell boundaries and the factor is selected to

be in the upper end of the range for slides in which the stain is pervasively present throughout the

slide. In this way, as areas are encountered on the

slide with greater or lower background intensities,

the threshold may be raised or lowered to help reduce

background objects. With this method, the threshold

changes in step with the aging of the light source

such that the effects of the aging are canceled out.

The image matrix resulting at 316 from the

thresholding step is a binary image of black (0) 5

and white (255) pixels.

As is often the case with thresholding

operations such as that described above, some

undesired areas will lie above the threshold value

due to noise, small stained cell fragments, and other

artifacts. It is desired and possible to eliminate

these artifacts by virtue of their small size

compared with legitimate cells of interest.

Morphological processes are utilized to perform this

function.

Morphological processing is similar to the low

pass filter convolution process described earlier except that it is applied to a binary image. Similar

to spatial convolution, the morphological process

traverses an input image matrix, pixel by pixel, and

places the processed pixels in an output matrix.

Rather than calculating a weighted sum of neighboring

pixels as m the low pass convolution process, the

morphological process uses set theory operations to

combine neighboring pixels in a nonlinear fashion.

Erosion is a process whereby a single pixel

layer is taken away from the edge of an object.

Dilation is the opposite process which adds a single

pixel layer to the edges of an object. The power of

morphological processing is that it provides for

further discrimination to eliminate small objects

that have survived the thresholding process and yet

are not likely tumor cells . The erosion and dilation

processes that make up a morphological "open"

preferably make small objects disappear yet allows

large objects to remain. Morphological processing of

binary images is described in detail in "Digital Image Processing", pages 127-137, G.A. Baxes, John

Wiley & Sons (1994) .

Fig. 19 illustrates the flow diagram for this

process. As shown here, a morphological "open"

process performs this suppression. A single

morphological open consists of a single morphological

erosion 320 followed by a single morphological

dilation 322. Multiple "opens" consist of multiple

erosions followed by multiple dilations. In the

preferred embodiment, one or two morphological opens

are found to be suitable . At this point in the

processing chain, the processed image contains

thresholded objects of interest, such as tumor cells

(if any were present in the original image) , and

possibly some residual artifacts that were too large

to be eliminated by the processes above.

Fig. 20 provides a flow diagram illustrating a

blob analysis performed to determine the number,

size, and location of objects in the thresholded

image. A blob is defined as a region of connected

pixels having the same "color," in this case, a value of 255. Processing is performed over the

entire image to determine the number of such regions

at 324 and to determine the area and x,y coordinates

for each detected blob at 326. Comparison of the

size of each blob to a known minimum area at 328 for

a tumor cell allows a refinement in decisions about

which objects are objects of interest, such as tumor

cells, and which are artifacts. The location

(x,y coordinates) of objects identified as cells of

interest in this stage are saved for the final 40x

reimaging step described below. Objects not passing

the size test are disregarded as artifacts.

The processing chain described above identifies

objects at the scanning magnification as cells of

interest candidates. As illustrated in Fig. 21, at

the completion of scanning, the system switches to

the 4Ox magnification objective at 330, and each

candidate is reimaged to confirm the identification

332. Each 40x image is reprocessed at 334 using the

same steps as described above but with test

parameters suitably modified for the higher magnification (e.g., area). At 336, a region of

interest centered on each confirmed cell is saved to

the hard drive for review by the pathologist .

As noted earlier, a mosaic of saved images is

made available for viewing by the pathologist. As

shown in Fig. 22, a series of images of cells which

have been confirmed by the image analysis is

presented in the mosaic 150. The pathologist can

then visually inspect the images to make a

determination whether to accept (152) or reject (153)

each cell image. Such a 5 determination can be noted

and saved with the mosaic of images for generating a

printed report .

In addition to saving the image of the cell and

its region, the cell coordinates are saved should the

pathologist wish to directly view the cell through

the oculars or on the image monitor. In this case,

the pathologist reloads the slide carrier, selects

the slide and cell for review from a mosaic of cell

images, and the system automatically positions the

cell under the microscope for viewing. It has been found that normal cells whose nuclei

have been stained with hematoxylin are often quite

numerous , numbering in the thousands per lOx image .

Since these cells are so numerous, and since they

tend to clump, counting each individual nucleated

cell would add an excessive processing burden, at the

expense of speed, and would not necessarily provide

an accurate count due to clumping. The apparatus

performs an estimation process in which the total

area of each field that is stained hematoxylin blue

is measured and this area is divided by the average

size of a nucleated cell. Fig. 23 outlines this

process. In this process, a single color band (the

red channel provides the best contrast for blue

stained nucleated cells) is processed by calculating

the average pixel value for each field at 342,

establishing two threshold values (high and low) as

indicated at 344, 346, and counting the number of

pixels between these two values at 348. In the

absence of dirt, or other opaque debris, this

provides a count of the number of predominantly blue pixels . By dividing this value by the average area

for a nucleated cell at 350, and looping over all

fields at 352, an approximate cell count is obtained.

Preliminary testing of this process indicates an

accuracy with +/- 15%. It should be noted that for

some slide preparation techniques, the size of

nucleated cells can be significantly larger than the

typical size. The operator can select the

appropriate nucleated cell size to compensate for

these characteristics.

As with any imaging system, there is some loss

of modulation transfer (i.e., contrast) due to the

modulation transfer function (MTF) characteristics of

the imaging optics, camera, electronics, and other

components. Since it is desired to save "high

quality" images of cells of interest both for

pathologist review and for archival purposes, it is

desired to compensate for these MTF losses. An MTF

compensation, or MTFC, is performed as a digital

process applied to the acquired digital images. A

digital filter is utilized to restore the high spatial frequency content of the images upon storage,

while maintaining low noise levels. With this MTFC

technology, image quality is enhanced, or restored,

through the use of digital processing methods as

opposed to conventional oil-immersion or other

hardware based methods. MTFC is described further in

"The Image Processing Handbook," pages 225 and 337,

J. C. Rues, CRC Press (1995) .

Referring to Fig. 24, the functions available in

a user interface of the apparatus 10 are shown. From

the user interface, which is presented graphically on

computer monitor 26, an operator can select among

apparatus functions which include acquisition 402,

analysts 404, and system configuration 406. At the

acquisition level 402, the operator can select

between manual 408 and automatic 410 modes of

operation. In the manual mode, the operator is

presented with manual operations 409. Patient

information 414 regarding an assay can be entered at

412. In the analysis level 404, review 416 and

report 418 functions are made available. At the review level 416, the operator can select a montage

function 420. At this montage level, a pathologist

can perform diagnostic review functions including

visiting an image 422, accept/reject of cells 424,

nucleated cell counting 426, accept/reject of cell

counts 428, and saving of pages at 430. The report

level 418 allows an operator to generate patient

reports 432. In the configuration level 406, the

operator can select to configure preferences at 434,

input operator information 437 at 436, create a

system log at 438, and toggle a menu panel at 440.

The configuration preferences include scan area

selection functions at 442, 452; montage

specifications at 444, bar code handling at 446,

default cell counting at 448, stain selection at 450,

and scan objective selection at 454.

Computer Implementation

Aspects of the invention may be implemented in

hardware or software, or a combination of both.

However, preferably, the algorithms and processes of

the invention are implemented in one or more computer

programs executing on programmable computers each

comprising at least one processor, at least one data

storage system (including volatile and non-volatile

memory and/or storage elements) , at least one input

device, and at least one output device. Program code

is applied to input data to perform the functions

described herein and generate output information.

The output information is applied to one or more

output devices, in known fashion.

Each program may be implemented in any desired

computer language (including machine, assembly, high

level procedural, or object oriented programming

languages) to communicate with a computer system. In

any case, the language may be a compiled or

interpreted language . Each such computer program is preferably stored

on a storage media or device (e.g., ROM, CD-ROM,

tape, or magnetic diskette) readable by a general or

special purpose programmable computer, for

configuring and operating the computer when the

storage media or device is read by the computer to

perform the procedures described herein. The

inventive system may also be considered to be

implemented as a computer-readable storage medium,

configured with a computer program, where the storage

medium so configured causes a computer to operate in

a specific and predefined manner to perform the

functions described herein.

A number of embodiments of the present invention

have been described. Nevertheless, various

modifications may be made without departing from the

spirit and scope of the invention. Accordingly, the

invention is not to be limited by the specific

illustrated embodiment, but only by the scope of the

appended claims .

Claims

CLAIMSWhat is claimed:

1. A method for automated detection of a cell

proliferative disorder, comprising,

providing a sample on a slide, wherein the sample is

stained with an antibody to a protein associated with

a cell proliferative disorder;

identifying a processing parameter for the

stained sample;

scanning the stained sample at a plurality

of locations at a low magnification on an

optical system;

acquiring a low magnification image at the

low magnification at each location in the

scanned area;

processing each low magnification image to

detect a candidate objects of interest;

storing stage coordinates of each location

for each candidate object of interest; adjusting the optical system to a higher

magnification;

repositioning the stage to the location for

each candidate object of interest;

acquiring a higher magnification image of

each candidate object of interest; and

storing each higher magnification image.

2. The method of claim 1, wherein the cell

proliferative disorder is a neoplasm.

3. The method of claim 1, wherein the cell

proliferative disorder is breast cancer.

4. The method of claim 1, wherein the antibody is

selected from the group consisting of an anti-

HER2/neu antibody, anti-estrogen receptor

antibody, anti-progesterone receptor antibody,

anti-p53 antibody and anti-cyclin Dl antibody.

5. The method of claim 1, wherein the processing

parameter is selected from the group consisting

of a positive control, negative control or test

sample .

6. The method of claim 1, wherein the detection of

the candidate object of interest is by size,

color or shape .

7. An automated system for quantification of a cell

proliferative disorder in a sample, comprising:

a slide configured to accept a sample,

wherein the sample is stained with an antibody

to a protein associated with a cell

proliferative disorder;

a code associated with the slide for

identification of the slide's processing

parameters ;

a lens for magnification of the sample on

the slide; a stage configured to hold the slide,

wherein the stage is optically coupled to the

lens ;

a camera optically coupled to the lens

configured to acquire an image from the lens;

a computer program residing on a computer-

readable medium for

executing the processing parameters of

the code;

selecting a position on the stage

identifying a candidate object of

interest based upon color, shape or size;

obtaining a low magnification image of

the candidate object of interest;

storing the position of the candidate

object of interest;

adjusting the magnification of the

lens ;

repositioning the stage to the position

of the candidate object of interest; obtaining a higher magnification image

of the candidate object of interest;

storing the higher magnification image;

allowing retrieval of the images

acquired; and

a monitor configured to view the

images .

8. The system of claim 7, wherein the code is a bar

code .

9. The system of claim 7, wherein the lens is an

objective of a microscope.

10. The system of claim 7, wherein the camera is a

CCD camera .

11. A computer program, residing on a computer-

readable medium, comprising instructions for

causing a computer to:

execute a processing parameter of a slide; select a position on a stage of an optical

system;

identify a candidate object of interest

based upon color, shape or size;

obtain a low magnification image of the

candidate object of interest ,-

store the position of the candidate object

of interest;

adjust the magnification of the optical

system;

reposition the stage to the position of the

candidate object of interest;

obtain a higher magnification image of the

candidate object of interest;

store the higher magnification image; and

allow retrieval of the images acquired.

12. An apparatus comprising a computer-readable

storage medium tangibly embodying program

instruction for quantifying a cell proliferative

disorder, the program instruction inducing instruction operable for causing an optical

system to:

execute a processing parameter of a slide;

select a position on a stage of an optical

system;

identify a candidate object of interest

based upon color, shape or size;

obtain a low magnification image of the

candidate object of interest;

store the position of the candidate object

of interest;

adjust the magnification of the optical

system;

reposition the stage to the position of the

candidate object of interest;

obtain a higher magnification image of the

candidate object of interest;

store the higher magnification image; and

allow retrieval of the images acquired.

3. The apparatus of claim 12, wherein the image is

a digital image .