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Proceedings Paper

Segmentation of digitized histological sections for vasculature quantification in the mouse hind limb
Author(s): Yiwen Xu; J. Geoffrey Pickering; Zengxuan Nong; Aaron D. Ward
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Paper Abstract

Characteristics of microvasculature can be revealed by immunohistochemical tissue staining, but manual quantification of these characteristics on whole-slide images containing potentially hundreds of vessels on each is tedious and subject to operator variability. Our objective was to develop and validate a fully automated segmentation of the vascular smooth muscle layer on whole-section histology of wild type and regenerated post-ischemia mouse hind limb microvasculature, stained for smooth muscle using 3,3'-Diaminobenzidine (DAB) immunostain. Major challenges to this segmentation are the irregularity of vessel wall staining, resulting in apparent fragmentation of intact vessels on the images, and artefactual appearance of stain on structures other than vessel walls. The automated segmentation localizes these fragments by color deconvolution to isolate the DAB stain. Complete vessel walls were reconstituted by joining of the topological skeletons of the vessel wall fragments. Based on a highly accurate registration of serial histology sections previously developed in our lab, artefactual fragments were removed based on measured incoherence with neighboring tissue in 3D. The vessel wall thickness, and vessel count, density, area, and perimeter were quantified. For segmentation validation, vessels were manually delineated and compared to the automated segmentation approach on a wild type mouse, with a dice similarity coefficient of 0.84. Differences were observed in the vessel measurements between the wild type and the regenerated vasculature in post-ischemic mice, as expected. Fully automatic and accurate measures of the vascular morphology are feasible with the automated segmentation of the vascular smooth muscle.

Paper Details

Date Published: 19 March 2015
PDF: 7 pages
Proc. SPIE 9420, Medical Imaging 2015: Digital Pathology, 942003 (19 March 2015); doi: 10.1117/12.2081506
Show Author Affiliations
Yiwen Xu, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
J. Geoffrey Pickering, The Univ. of Western Ontario (Canada)
Robarts Research Institute (Canada)
Zengxuan Nong, Robarts Research Institute (Canada)
Aaron D. Ward, The Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 9420:
Medical Imaging 2015: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

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