Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks

Abstract

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.