Publication
Pattern Recognition
Paper

Your article is registered as belonging to the Special Issue/Collection entitled “SI: MLMI”. If this is NOT correct and your article is a regular item or belongs to a different Special Issue please contact h.searle@elsevier.com immediately prior to returning your corrections. Semi-supervised learning and graph cuts for consensus based medical image segmentation

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Abstract

Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL). Popular approaches use iterative Expectation Maximization (EM) to estimate the final annotation and quantify annotator's performance. Such techniques pose the risk of getting trapped in local minima. We propose a self consistency (SC) score to quantify annotator consistency using low level image features. SSL is used to predict missing annotations by considering global features and local image consistency. The SC score also serves as the penalty cost in a second order Markov random field (MRF) cost function optimized using graph cuts to derive the final consensus label. Graph cut obtains a global maximum without an iterative procedure. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than competing methods.

Date

01 Mar 2017

Publication

Pattern Recognition

Authors

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