Non-uniform object counting method in large-format pyramid images applied to CD31 vessel counting in whole-mount digital pathology sections

Mayan Murray; Melissa L. Hill; Kela Liu; James G. Mainprize; Martin J. Yaffe

doi:10.1117/12.2216817

23 March 2016 Non-uniform object counting method in large-format pyramid images applied to CD31 vessel counting in whole-mount digital pathology sections

Mayan Murray, Melissa L. Hill, Kela Liu, James G. Mainprize, Martin J. Yaffe

Author Affiliations +

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97910D (2016) https://doi.org/10.1117/12.2216817
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Whole-mount pathology imaging has the potential to revolutionize clinical practice by preserving context lost when tissue is cut to fit onto conventional slides. Whole-mount digital images are very large, ranging from 4GB to greater than 50GB, making concurrent processing infeasible. Block-processing is a method commonly used to divide the image into smaller blocks and process them individually. This approach is useful for certain tasks, but leads to over-counting objects located on the seams between blocks. This issue is exaggerated as the block size decreases. In this work we apply a novel technique to enumerate vessels, a clinical task that would benefit from automation in whole-mount images. Whole-mount sections of rabbit VX2 tumors were digitized. Color thresholding was used to segment the brown CD31- DAB stained vessels. This vessel enumeration was applied to the entire whole-mount image in two distinct phases of block-processing. The first (whole-processing) phase used a basic grid and only counted objects that did not intersect the block’s borders. The second (seam-processing) phase used a shifted grid to ensure all blocks captured the block-seam regions from the original grid. Only objects touching this seam-intersection were counted. For validation, segmented vessels were randomly embedded into a whole-mount image. The technique was tested on the image using 24 different block-widths. Results indicated that the error reaches a minimum at a block-width equal to the maximum vessel length, with no improvement as the block-width increases further. Object-density maps showed very good correlation between the vessel-dense regions and the pathologist outlined tumor regions.

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Mayan Murray, Melissa L. Hill, Kela Liu, James G. Mainprize, and Martin J. Yaffe "Non-uniform object counting method in large-format pyramid images applied to CD31 vessel counting in whole-mount digital pathology sections", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910D (23 March 2016); https://doi.org/10.1117/12.2216817

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KEYWORDS

Image processing

Image segmentation

Tumors

Pathology

RGB color model

Optical filters

Binary data

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