Differentiation of arterioles from venules in mouse histology images using machine learning

J. Sachi Elkerton; Yiwen Xu; J. Geoffrey Pickering; Aaron D. Ward

doi:10.1117/12.2217178

23 March 2016 Differentiation of arterioles from venules in mouse histology images using machine learning

J. Sachi Elkerton, Yiwen Xu, J. Geoffrey Pickering, Aaron D. Ward

Author Affiliations +

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97910G (2016) https://doi.org/10.1117/12.2217178
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

J. Sachi Elkerton, J. Sachi Elkerton, Yiwen Xu, Yiwen Xu, J. Geoffrey Pickering, J. Geoffrey Pickering, Aaron D. Ward, Aaron D. Ward, "Differentiation of arterioles from venules in mouse histology images using machine learning", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910G (23 March 2016); doi: 10.1117/12.2217178; https://doi.org/10.1117/12.2217178

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