28 February 2017 Differentiation of arterioles from venules in mouse histology images using machine learning
Author Affiliations +
Abstract
Analysis and morphological comparison of the arteriolar and venular components of a microvascular network 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 with smooth muscle α -actin. Classifiers trained on statistical and morphological features by supervised machine learning provided useful classification accuracy for differentiation of arterioles from venules, achieving an area under the receiver operating characteristic curve of 0.89. Feature selection was consistent across cross validation iterations, and a small set of two features was required to achieve the reported performance, suggesting the 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 of the arteriolar and venular networks in the mouse.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
J. Sachi Elkerton, Yiwen Xu, J. Geoffrey Pickering, Aaron D. Ward, "Differentiation of arterioles from venules in mouse histology images using machine learning," Journal of Medical Imaging 4(2), 021104 (28 February 2017). https://doi.org/10.1117/1.JMI.4.2.021104 . Submission: Received: 1 July 2016; Accepted: 12 December 2016
Received: 1 July 2016; Accepted: 12 December 2016; Published: 28 February 2017
JOURNAL ARTICLE
10 PAGES


SHARE
Back to Top