Elkerton J Sachi, Xu Yiwen, Pickering J Geoffrey, Ward Aaron D
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, 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 Med Imaging (Bellingham). 2017 Apr;4(2):021104. doi: 10.1117/1.JMI.4.2.021104. Epub 2017 Feb 28.
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 [Formula: see text]-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.
对微血管网络的小动脉和小静脉成分进行分析和形态学比较，对于我们理解影响每个器官系统的多种疾病至关重要。我们开发并评估了首个全自动软件系统，用于在小鼠后肢经平滑肌α-肌动蛋白免疫染色的高分辨率数字组织学图像上区分小动脉和小静脉。通过监督机器学习在统计和形态学特征上训练的分类器，在区分小动脉和小静脉方面提供了有用的分类准确率，受试者操作特征曲线下面积达到0.89。特征选择在交叉验证迭代中是一致的，并且只需一小套两个特征就能实现报告的性能，这表明该系统具有通用性。该系统消除了对典型样本中出现的数百个微血管进行费力的手动分类的需求，并为小鼠小动脉和小静脉网络的高通量分析铺平了道路。
