Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks

Automated pancreas segmentation in medical images is a prerequisite for many clinical applications, such as diabetes inspection, pancreatic cancer diagnosis, and surgical planing. In this paper, we formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN). Our approach conducts pancreatic detection and boundary segmentation with two types of CNN models respectively: (1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; (2) the boundary detection step to allocate the semantic boundaries of pancreas. Both detection results of the two networks are fused together as the initialization of a conditional random field (CRF) framework to obtain the final segmentation output. Our approach achieves the mean dice similarity coefficient (DSC) \(76.1\,\%\) with the standard deviation of \(8.7\,\%\) in a dataset containing 78 abdominal MRI scans. The proposed algorithm achieves the best results compared with other state of the arts.