Simplified labeling process for medical image segmentation

Authors:
Mingchen Gao;Junzhou Huang;Xiaolei Huang;Shaoting Zhang;Dimitris N. Metaxas
Affiliations:
CBIM Center, Rutgers University, Piscataway, NJ;Department of Computer Science and Engineering, University of Texas at Arlington, TX;Computer Science and Enginnering Department, Lehigh University, PA;CBIM Center, Rutgers University, Piscataway, NJ;CBIM Center, Rutgers University, Piscataway, NJ
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

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Abstract

Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms.