title: 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.
authors.affiliation: Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Jinzheng
authors.initials: J
authors.lastname: Cai
authors.affiliation: Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20892, USA.
authors.forename: Le
authors.initials: L
authors.lastname: Lu
authors.affiliation: Department of Computer Information and Science Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Zizhao
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Fuyong
authors.initials: F
authors.lastname: Xing
authors.affiliation: Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA; Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA; Department of Computer Information and Science Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.affiliation: Department of Radiology, Tangdu Hospital, the Fourth Military Medical University, Xi'an 710038, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Yin
mesh_terms: D000465:Algorithms; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016571:Neural Networks, Computer; D010179:Pancreas / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity