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Proceedings Paper

Differentiation of arterioles from venules in mouse histology images using machine learning
Author(s): J. Sachi Elkerton; Yiwen Xu; J. Geoffrey Pickering; Aaron D. Ward
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Paper Abstract

Analysis and morphological comparison of arteriolar and venular networks are essential to our understanding of multiple diseases affecting every organ system. We have developed and evaluated the first fully automatic software system for differentiation of arterioles from venules on high-resolution digital histology images of the mouse hind limb immunostained for smooth muscle α-actin. Classifiers trained on texture and morphologic features by supervised machine learning provided excellent classification accuracy for differentiation of arterioles and venules, achieving an area under the receiver operating characteristic curve of 0.90 and balanced false-positive and false-negative rates. Feature selection was consistent across cross-validation iterations, and a small set of three features was required to achieve the reported performance, suggesting potential generalizability of the system. This system eliminates the need for laborious manual classification of the hundreds of microvessels occurring in a typical sample, and paves the way for high-throughput analysis the arteriolar and venular networks in the mouse.

Paper Details

Date Published: 23 March 2016
PDF: 7 pages
Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910G (23 March 2016); doi: 10.1117/12.2217178
Show Author Affiliations
J. Sachi Elkerton, Western Univ. (Canada)
Regional Cancer Program (Canada)
Yiwen Xu, Western Univ. (Canada)
Robarts Research Institute (Canada)
Regional Cancer Program (Canada)
J. Geoffrey Pickering, Western Univ. (Canada)
Robarts Research Institute (Canada)
Aaron D. Ward, Western Univ. (Canada)
Regional Cancer Program (Canada)


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

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