Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
This addresses the need for reliable ground truth in medical image segmentation, though it is incremental as it builds on existing graph cut and semi-supervised learning techniques.
The paper tackles the problem of deriving consensus segmentations from multiple expert annotations in medical imaging by proposing a method using semi-supervised learning and graph cuts, with results showing accurate and more consistent segmentations than competing methods on synthetic and real datasets.
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.