System and method for finding regions of interest for microscopic digital montage imaging

Abstract

A 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.

Description

FIELD OF THE INVENTION

[0001] The present invention relates to microscopic digital imaging of complete tissue sections for medical and research use. In particular it describes a method to find regions of interest for high throughput montage imaging of microscope slides using a standard microscope and cameras.

BACKGROUND OF THE INVENTION

[0002] Laboratories in many biomedical specialties, such as anatomic pathology, hematology, and microbiology, examine tissue under a microscope for the presence and the nature of disease. In recent years, these laboratories have shown a growing interest in microscopic digital imaging as an adjunct to direct visual examination. Digital imaging has a number of advantages including the ability to document disease, share findings, collaborate (as in telemedicine), and analyze morphologic findings by computer. Though numerous studies have shown that digital image quality is acceptable for most clinical and research use, some aspects of microscopic digital imaging are limited in application.

[0003] Perhaps the most important limitation to microscopic digital imaging is a “subsampling” problem encountered in all single frame images. 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. Significantly, 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.

[0004] To get around the limitations of single frame imaging, developers have looked at two general options. 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 remote control. Though such systems have been used successfully for telepathology, they do not lend themselves to documentation, collaboration, or computer based analysis.

[0005] 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.

[0006] 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 um/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.

[0007] A much more difficult limitation with the prior systems is an image capture time problem. Given an optical primary magnification of twenty and a two-third inch CCD, the system field of view is approximately (8.8 mm×6.6 mm)/20=0.44×0.33 mm. A standard microscope slide typically has a specimen area of 25 mm×50 mm or 12.5 square centimeters. This requires over eighty-six hundred fields to image this entire specimen region. However, the average tissue section for anatomic pathology is approximately 2.25 square centimeters. This only requires approximately fifteen hundred fields to cover the tissue alone, approximately 80 percent less fields.

[0008] Traditionally, field rate in montage systems is limited by 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.

[0009] Thus, a system is needed to automatically find the region of interest on a microscope slide and image only this region.

SUMMARY OF THE INVENTION

[0010] 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.

[0011] Accordingly, it is an object of the invention to provide a microscopic imaging system for whole slide montage in which standard microscope optics, off the shelf cameras and a simple motorized stage can be used to select the region of interest, image only this section and produce perfectly aligned image tiles.

[0012] 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.

[0013] It is another object of the invention to use a high-resolution slide scanning process to control the operation of the motorized stage and camera. 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.

[0014] Additional features and advantages of the invention will be set forth in the description that follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and advantages of the invention to be realized and attained by the microscopic image capture system will be pointed out in the written description and claims hereof as well as the appended drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention that together with the description serve to explain the principles of the invention.

[0016] FIG. 1 illustrates an isometric view of the system in a preferred embodiment;

[0017] FIG. 2 represents the results of the macroscopic image after the cropping component has been applied to remove non-slide regions;

[0018] FIG. 3 represents the results of the find tissue component; and

[0019] FIG. 4 is an overlay of FIG. 2 and 3 representing the regions of the slide to be imaged.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The following paragraphs describe the functionality of the inventive system and method for high throughput montage imaging of microscope slides using a standard microscope and cameras.

[0021] FIG. 1 illustrates a preferred embodiment of the invention. In this embodiment, 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.

[0022] As is obvious to one skilled in the art, although capturing the single macroscopic image saves time, it is not necessary for the operation of the invention. Multiple macroscopic images may be required to generate control parameters to the accuracy required based on the ratio of the macroscopic to microscopic magnifications and the camera specifications of each camera, if separate cameras are utilized.

[0023] Specifically in a preferred embodiment, 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.

[0024] 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.

[0025] 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. For this discussion, the slide or region of interest is assumed to be positioned near the center, vertically, in the thumbnail image. To facilitate this and subsequent processing steps, 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.

[0026] Since the slide may not be oriented perfectly horizontal in the original thumbnail image, the identified slide edges are likely to lie at an angle. Thus, even after cropping, there may be remnants of the slide edges or cover slip in the cropped image. Therefore, 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.

[0027] 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. In order to minimize the time and storage space required to accomplish high-resolution slide imaging, 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.

[0028] The tissue finding component identifies tissue regions via a sequence of filters that incorporate knowledge of the typical appearance and location of tissue and nontissue 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. Subsequently, morphological filters are applied to refine the classification based on the size and position of neighboring groups of potential tissue pixels.

[0029] The filters which comprise the tissue finding component process the pixels of the cropped grayscale thumbnail image 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.

[0030] The above description was based on using the mean and standard deviation of the local pixels as the basis for detecting regions of interest. It is obvious to one skill in the art that other image characteristics can be also used to identify the specimen from nonitems of interest such as dust and scratches.

[0031] This description was also based on processing a gray scale macroscopic image, the same processing tools can be applied to each of the color components (traditionally, red, green and blue) of a color image. Additional processing tools can also be applied between the color components to refine the tissue finding accuracy and to remove features such as labels and writing that are not critical to the application.

[0032] An example of the image cropping and find tissue processing are shown in FIG. 23 and 4. 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.

[0033] At the completion of the find tissue component, 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.

[0034] The foregoing description has been directed to specific embodiments of this invention. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the invention.

Claims

1. A system for processing a thumbnail image from a microscope slide to determine tissue locations on the slide, the system comprises: a plurality of first components for identifying tissue regions and determining locations of tissue on the slide; a motorized stage where a frame image containing the entire slide is taken with a camera; and means for using information about the locations to generate control parameters for the motorized stage and the camera to ensure that a scanning process captures high quality images of only the tissue regions.

2. The system of claim 1, wherein the plurality of first components include an image cropping component for identifying tissue regions on the slide to be scanned, wherein the image cropping component: determines an approximate location of a slide boundary by searching upper and lower intervals corresponding to regions expected to contain upper and lower edges of the slide; re-examines the approximate location to determine a more accurate location; and removes portions of the image falling outside of the determined slide boundary.

3. The system of claim 2, wherein the image cropping component converts a copy of the thumbnail image to a grayscale image.

4. The system of claim 2, wherein the image cropping component crops a color thumbnail image at estimated edge locations, wherein multiple values of each pixel in the color thumbnail image are used to achieve better results by identifying spurious features on the slide.

5. The system of claim 4, wherein the image cropping component crops the color thumbnail image at estimated boundary locations and uniformly reduces the color thumbnail image size to produce a small thumbnail image of the slide for rapid visual slide identification.

6. The system of claim 3, wherein the image cropping component identifies pixel blocks that are likely to contain remaining boundary edges and flags these blocks as edges that should not be considered for high-resolution imaging.

7. The system of claim 1, wherein the plurality of first components include a tissue finding component that locates regions in the thumbnail image that contain tissue of interest to a specialist.

8. The system of claim 7, wherein a cropped grayscale image is inputted into the tissue finding component from a imaging cropping component, wherein 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 and outputs a tiling matrix whose values indicate which tiles should be imaged.

9. The system of claim 8, wherein a first filter analyzes mean and standard deviation of local pixel intensities and combines the mean and the standard deviation to generate a threshold value for making an initial classification of tissue versus non-tissue regions.

10. The system of claim 9, wherein the intensities are used to differentiate tissue-containing regions from blank regions and other non-tissue containing regions.

11. The system of claim 9, wherein the standard deviation represents the amount of variation in pixel values and is a good indicator of the border between tissue and the blank slide.

12. The system of claim 8, wherein morphological filters are applied to threshold standard deviation data to refine classification based on size and position of neighboring groups of potential tissue pixels, wherein the morphological filters process pixels of the cropped grayscale image in groups that correspond to slide regions that can be imaged individually during a high-resolution scanning process.

13. The system of claim 12, wherein the morphological filters ensure that tiles that partially filled with tissue are classified as tissue-containing tiles.

14. The system of claim 8, wherein other image characteristics can be used to identify tissue from non-items of interest.

15. The system of claim 1, wherein the frame image is a single macroscopic image.

16. The system of claim 1, wherein the frame image is multiple macroscopic images.

17. The system of claim 1, wherein the plurality of first components includes a scan control component that interprets a tile matrix, outputted by a find tissue component, and transposes positions of the tile matrix into actual stage coordinates for a microscopic imaging.

18. A method for processing a thumbnail image from a microscope slide to determine tissue locations on the slide, the method comprises the steps of: flat-field correcting the thumbnail image using a blank slide and a similar image obtained from a camera that captured the thumbnail image; cropping the thumbnail image by an image cropping component; inputting a cropped grayscale image into a tissue finding component, wherein 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 and outputs a tiling matrix whose values indicate which tiles should be imaged; and interpreting the tiling matrix, by a scan control component, and transposing positions of the tiling matrix into actual stage coordinate for a microscopic imaging.

19. The method of claim 18, wherein the step of cropping further comprises the steps of: determining an approximate location of a slide boundary by searching upper and lower intervals corresponding to regions expected to contain upper and lower edges of the slide; re-examining the approximate location to determine a more accurate location; and removing portions of the image falling outside of the determined slide boundary.

20. The method of claim 19, further comprising the step of converting a copy of the thumbnail image to a grayscale image.

21. The method of claim 19, further comprising the step of cropping a color thumbnail image at estimated edge locations, wherein multiple values of each pixel in the color thumbnail image are used to achieve better results by identifying spurious features on the slide.

22. The method of claim 19, further comprising the step of cropping the color thumbnail image at estimated boundary locations and uniformly reducing the color thumbnail image size to produce a small thumbnail image of the slide for rapid visual slide identification.

23. The method of claim 20, further comprising the steps of identifying pixel blocks that are likely to contain remaining boundary edges and flagging these blocks as edges that should not be considered for high resolution imaging.

24. The method of claim 18, further comprising the steps of analyzing mean and standard deviation of local pixel intensities and combining the mean and the standard deviation to generate a threshold value.

25. The method of claim 24, further comprising the step of using the intensities to differentiate tissue-containing regions from blank regions and other nontissue containing regions.

26. The method of claim 18, further comprising the step of applying morphological filters to threshold standard deviation data to refine classification based on size and position of neighboring groups of potential tissue pixels, whereby the morphological filters process pixels of the cropped grayscale image in groups that correspond to slide regions that can be imaged individually during a high-resolution scanning process.

27. A system for processing a thumbnail image from a microscope slide to determine tissue locations on the slide, the system comprises: an image cropping component for cropping the thumbnail image; a tissue finding component that identifies tissue regions by applying a sequence of filters that incorporate knowledge of typical appearance and location of tissue and non-tissue slide regions and outputs a tiling matrix whose values indicate which tiles should be imaged; a scan control component for interpreting the tiling matrix; and means for inputting a cropped image from the image cropping component into the tissue finding component and for transposing positions of the tiling matrix into actual stage coordinate, by the scan control component, for a microscopic imaging.