Crohn's disease tissue segmentation from abdominal MRI using semantic information and graph cuts

Abstract

We propose a graph cut based method to segment regions in abdominal magnetic resonance (MR) images affected with Crohn's disease (CD). Intensity, texture, curvature and context information are used with random forest (RF) classifiers to calculate probability maps for graph cut segmentation. The RF classifiers also provide semantic information used to design a novel smoothness cost. Experimental results on 26 real patient data shows our method accurately segments the diseased areas. Inclusion of semantic information significantly improves segmentation accuracy and its importance is reflected in quantitative measures and visual results.