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

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

doi:10.1117/1.JMI.4.2.021104

28 February 2017 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 +

Journal of Medical Imaging, Vol. 4, Issue 2, 021104 (February 2017). https://doi.org/10.1117/1.JMI.4.2.021104

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.

Citation Download 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," Journal of Medical Imaging 4(2), 021104 (28 February 2017). https://doi.org/10.1117/1.JMI.4.2.021104

Received: 1 July 2016; Accepted: 12 December 2016; Published: 28 February 2017

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available