Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images

• Published in: AMIA Summits on Translational Science proceedings Vol. 2017; pp. 227 - 236
• Main Authors: Wen, Si, Kurc, Tahsin M, Hou, Le, Saltz, Joel H, Gupta, Rajarsi R, Batiste, Rebecca, Zhao, Tianhao, Nguyen, Vu, Samaras, Dimitris, Zhu, Wei
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2018
• ISSN: 2153-4063; 2153-4063

Segmentation of nuclei in whole slide tissue images is a common methodology in pathology image analysis. Most segmentation algorithms are sensitive to input algorithm parameters and the characteristics of input images (tissue morphology, staining, etc.). Because there can be large variability in the color, texture, and morphology of tissues within and across cancer types (heterogeneity can exist even within a tissue specimen), it is likely that a set of input parameters will not perform well across multiple images. It is, therefore, highly desired, and necessary in some cases, to carry out a quality control of segmentation results. This work investigates the application of machine learning in this process. We report on the application of active learning for segmentation quality assessment for pathology images and compare three classification methods, Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN), for their performance improvement and efficiency.