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Proceedings Paper

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

Paper Details

Date Published: 23 March 2016
PDF: 9 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910D (23 March 2016); doi: 10.1117/12.2216817
Show Author Affiliations
Mayan Murray, Sunnybrook Research Institute (Canada)
Melissa L. Hill, Sunnybrook Research Institute (Canada)
Kela Liu, Sunnybrook Research Institute (Canada)
James G. Mainprize, Sunnybrook Research Institute (Canada)
Martin J. Yaffe, Sunnybrook Research Institute (Canada)
Univ. of Toronto (Canada)


Published in SPIE Proceedings Vol. 9791:
Medical Imaging 2016: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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