Special Section on Digital Pathology

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

[+] Author Affiliations
J. Sachi Elkerton, Aaron D. Ward

Western University, Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

Baines Imaging Research Laboratory, London Regional Cancer Program, 800 Commissioners Road East, London, Ontario N6A 5W9, Canada

Yiwen Xu

Western University, Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

Baines Imaging Research Laboratory, London Regional Cancer Program, 800 Commissioners Road East, London, Ontario N6A 5W9, Canada

Western University, Robarts Research Institute, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

J. Geoffrey Pickering

Western University, Department of Medical Biophysics, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

Western University, Robarts Research Institute, 1151 Richmond Street, London, Ontario N6A 3K7, Canada

J. Med. Imag. 4(2), 021104 (Feb 28, 2017). doi:10.1117/1.JMI.4.2.021104
History: Received July 1, 2016; Accepted December 12, 2016
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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.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

J. Sachi Elkerton ; Yiwen Xu ; J. Geoffrey Pickering and Aaron D. Ward
"Differentiation of arterioles from venules in mouse histology images using machine learning", J. Med. Imag. 4(2), 021104 (Feb 28, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.021104


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