  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.
