WO2002056256A2 - A system and method for finding regions of interest for microscopic digital montage imaging - Google Patents
A system and method for finding regions of interest for microscopic digital montage imaging Download PDFInfo
- Publication number
- WO2002056256A2 WO2002056256A2 PCT/US2002/000458 US0200458W WO02056256A2 WO 2002056256 A2 WO2002056256 A2 WO 2002056256A2 US 0200458 W US0200458 W US 0200458W WO 02056256 A2 WO02056256 A2 WO 02056256A2
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- tissue
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/693—Acquisition
Definitions
- the present invention relates to microscopic digital imaging of complete tissue sections for medical and research use.
- it describes a method to find regions of interest for high throughput montage imaging of microscope slides using a standard microscope and cameras.
- the sub-sampling problem has two components: a field of view problem and a resolution-based problem.
- the field of view problem occurs when an investigator looking at a single frame cannot determine what lies outside the view of an image on a slide.
- the resolution-based problem occurs when the investigator looking at an image is limited to the resolution of the image. The investigator cannot "zoom in” for a closer examination or “zoom out” for a bird's eye view.
- the field of view and resolution-based problems are inversely related. Thus, as one increases magnification to improve resolution, one decreases the field of view. For example, as a general rule, increasing magnification by a factor of two decreases the field of view by a factor of four.
- the first option takes the general form of "dynamic-robotic" imaging, in which a video camera on the microscope transmits close to real time images to the investigator looking at a monitor, while the investigator operates the microscope by remove control. Though such systems have been used successfully for telepathology, they do not lend themselves to documentation, collaboration, or computer based analysis.
- the second option being investigated to overcome the limitations inherit in single frame imaging is a montage (or "virtual slide") approach. In this method, a robotic microscope systematically scans the entire slide, taking an image at every field. The individual images are then "knitted” together in a software application to form a very large data set with very appealing properties.
- the robotic microscope can span the entire slide area at a resolution limited only by the power of the optical system and camera.
- Software exists to display this data set at any resolution on a computer screen, allowing the user to zoom in, zoom out, and pan around the data set as if using a physical microscope.
- the data set can be stored for documentation, shared over the Internet, or analyzed by computer programs.
- the "virtual slide" option has some limitations, however. One of the limitations is file size. For an average tissue section, the data generated at 0.33 urn/pixel can be between two and five gigabytes uncompressed. In an extreme case, the data generated from one slide can be up to thirty- six gigabytes.
- field rate in montage systems is limited by the three factors - camera frame rate, image processing speed, and the rate of slide motion between fields. Given today's technology, the limiting factor can be reduced to only the camera frame rate. Using a 10 frame per second camera for the example above, imaging the entire slide would require 860 seconds or 14.33 minutes. If only the region of interest was imaged, this average time could be reduced to 150 seconds or 2.5 minutes; substantially increasing the slide throughput of an imaging system. Thus, a system is needed to automatically find the region of interest on a microscope slide and image only this region.
- the present invention relates to a method and system for processing a thumbnail image from a microscope slide to determine tissue locations on the slide.
- the system comprises an image cropping component, a tissue finding component, and a scan control component.
- the image cropping component crops the thumbnail image and removes portions of the image that fall outside of determined slide boundaries.
- the cropped image from the image cropping component is inputted into the tissue finding component.
- the tissue finding component identifies tissue regions by applying a sequence of filters that incorporate knowledge of typical appearance and location of tissue and non-tissue slide regions.
- the tissue finding component outputs a tiling matrix whose values indicate which tiles should be imaged.
- the scan control component interprets the tiling matrix and transposes positions of the tiling matrix into actual stage coordinate for a microscopic imaging.
- the present invention uses a pre-scan process applied to a macroscopic image of the entire slide, to guide a high-resolution slide scanning process and ensure high- quality images of the entire specimen are acquired.
- the pre-scan process includes an image cropping component, a tissue-finding component, and a scan control component.
- the image cropping and tissue finding components identify interesting regions on the slide to be scanned.
- the scan control component generates the control parameters for a motorized microscopic imaging system.
- This process utilizes information gathered by the pre-scan process, namely the imaging regions, to control the positioning of the stage to image only the regions of interest and to ensure the individual images are well aligned.
- Fig 1 illustrates an isometric view of the system in a preferred embodiment
- Fig. 2 represents the results of the macroscopic image after the cropping component has been applied to remove non-slide regions
- Fig. 3 represents the results of the find tissue component
- Fig. 4 is an overlay of Fig. 2 and 3 representing the regions of the slide to be imaged. DESCRIPTION OF THE PREFERRED EMBODIMENTS
- Fig. 1 illustrates a preferred embodiment of the invention.
- a slide 112 to be imaged is placed on a thumbnail imaging position in a slide holder on a motorized stage 102.
- a single frame image containing the entire slide is taken with a macro camera 106.
- This low-resolution image is analyzed by software components to determine the locations of tissue on slide 112. This information can then be used to generate control parameters for stage 102 and microscopic camera 104 to ensure that the scanning process captures high quality images of only the tissue regions, substantially reducing the time to scan an average slide.
- a pre-scan processing of the low- resolution or thumbnail image includes an image cropping component, a tissue- finding component and a scan control component.
- the image cropping component and tissue finding component identify tissue regions on the slide to be scanned.
- the scan control component generates the necessary control parameters to scan only the regions of interest under the microscopic optics.
- the first step in processing the thumbnail image consists of flat-field correcting the macroscopic thumbnail image using a similar image obtained from the same camera and a blank slide. This removes any spatial light anomalies from the thumbnail image, which may reduce the efficiency of the tissue-finding component. Given the format, or size, of the camera and the aspect ratio of the slide, a portion of the image will contain non-slide objects such as the slide carrier. To remove these features, the thumbnail image is cropped to extract only the slide information. The image cropping is accomplished via a two-pass process. The first pass determines an approximate location of the slide boundary, and the second pass fine- tunes this estimate. The search for the boundary is conducted over upper and lower intervals corresponding to the regions expected to contain the upper and lower slide edges, respectively.
- the slide or region of interest is assumed to be positioned near the center, vertically, in the thumbnail image.
- a copy of the thumbnail image is converted to grayscale. The portion of the image falling outside of the identified slide boundary is removed. It should be noted that the original color image is also cropped at the estimated edge locations, and then is uniformly reduced in size to produce a small thumbnail image of the slide for rapid, visual slide identification.
- the image-cropping component attempts to identify pixel blocks that likely contain these remaining edges and flags these blocks as edges that will not be considered for high resolution imaging by the tissue finding component.
- the resulting cropped grayscale image generated by the image-cropping component serves as input to the tissue finding component.
- This component locates regions in the thumbnail image that contain tissue of interest to a specialist.
- the inventive system captures only those regions of the slide that contain tissue. This approach requires that regions containing tissue be identified in the thumbnail image.
- the tissue finding component identifies tissue regions via a sequence of filters that incorporate knowledge of the typical appearance and location of tissue and non- tissue slide regions.
- Initial filtering steps analyze the mean and standard deviation of the local pixel intensities. Pixel mean intensities are used to differentiate tissue- containing regions from blank and other non-tissue regions, such as those containing the slide label or other markings.
- the standard deviation data represents the amount of variation in pixel values and thus is a good indicator of the border between tissue and the blank slide.
- the mean and standard deviation data is combined to generate a threshold value that is used to make an initial classification of tissue versus non- tissue.
- morphological filters are applied to refine the classification based on the size and position of neighboring groups of potential tissue pixels.
- the filters which comprise the tissue finding component process the pixels of the cropped grayscale thumbnail in groups that correspond to slide regions, or tiles, that can be imaged individually during the high-resolution scanning process. These filters ensure that tiles only partially filled with tissue are classified as tissue- containing tiles.
- the final output of the filter sequence is a tiling matrix whose values indicate which tiles should be imaged; the tiling matrix subsequently guides the high- resolution scanning process.
- FIG. 2 illustrates the macroscopic image after flat-field correction and image cropping.
- Fig. 3 illustrates the results of the find tissue component.
- the resulting tile matrix shown in Fig. 3 has a one-to-one correspondence to the field of view of the microscopic camera.
- White pixels (binary 1) signify field to be capture and black pixels represent regions not to image.
- Fig. 4 illustrates an overlay Fig. 2 and 3 representing the sections of the slide to be imaged. For this application (anatomical pathology), it is imperative to image all suspect regions that may contain tissue so conservative criteria were used in the find tissue component, resulting in cover slip edges and writing etched into the slide to be identified as to be imaged.
- the savings in the acquisition time is representative by the ratio of the white to black areas of Fig. 3. For this image, only 53% of the slide region is to be imaged, including the label and cover slip edges, and etched writing on the slide.
- the scan control component interprets the find tissue tile matrix (Fig. 3) and transposes the positions into actual stage coordinates for the microscopic imaging.
- a program running on a host computer controls the operation by communicating with a stage controller and microscopic camera 104. Actual scanning can occur in any fashion such as by rows or columns, or in a step fashion to image neighboring areas.
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AU2002237779A AU2002237779A1 (en) | 2001-01-11 | 2002-01-08 | A system and method for finding regions of interest for microscopic digital montage imaging |
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US09/758,037 US6993169B2 (en) | 2001-01-11 | 2001-01-11 | System and method for finding regions of interest for microscopic digital montage imaging |
US09/758,037 | 2001-01-11 |
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AU (1) | AU2002237779A1 (en) |
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Also Published As
Publication number | Publication date |
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US6993169B2 (en) | 2006-01-31 |
US7212660B2 (en) | 2007-05-01 |
AU2002237779A1 (en) | 2002-07-24 |
WO2002056256A3 (en) | 2003-02-13 |
US20100002918A1 (en) | 2010-01-07 |
US20020090120A1 (en) | 2002-07-11 |
US7421102B2 (en) | 2008-09-02 |
US7869641B2 (en) | 2011-01-11 |
US20060045320A1 (en) | 2006-03-02 |
US20060029266A1 (en) | 2006-02-09 |
US20080123919A1 (en) | 2008-05-29 |
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