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. 2016 Oct:9901:442-450.
doi: 10.1007/978-3-319-46723-8_51. Epub 2016 Oct 2.

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

Jinzheng Cai  1 , Le Lu  2 , Zizhao Zhang  3 , Fuyong Xing  4 , Lin Yang  5 , Qian Yin  6
Affiliations

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

Jinzheng Cai et al. Med Image Comput Comput Assist Interv. 2016 Oct.

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.

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Figures

Fig.1
Fig.1
The framework of our approach. CNN Training: CNN models are trained for pancreatic tissue allocation (the FCN model) and boundary detection (the HED model); CRF Training: A CRF model is learned based on the candidate regions that detected by CNN models. Testing: The segmentation begins with CNN models, and then will be further refined by the CRF model. The result of testing and the corresponding human annotation are displayed with the green and red dashed curves, respectively.
Fig. 2
Fig. 2
Sub-regions with weak and strong boundaries displayed in the first and third column, respectively. The corresponding manual annotations are displayed with red dashed curves.
Fig. 3
Fig. 3
The overview of our graphical model for decision fusion. The feature vector of each node is extracted from its corresponding candidate region of the HED map, the FCN map, and the MRI image. The null feature is defined to refer to the non-detected region in the FCN/HED maps. The graph nodes are then partitioned by graph inference. The result of decision fusion is displayed with the same color notation in Fig. 1.
Fig. 4
Fig. 4
(a) mean DSC with respect to probability threshold, which is applied to the output of CNN models; (b) comparison of segmentation accuracy between different approaches. The red lines and crosses are represent to the means and outliers, respectively. (the thresholds are set to the optimal in (a), DF is the acronym for decision fusion)
Fig.5
Fig.5
From left to right and top to bottom: Segmentation results with DSC from 95% to 51% using the same color notation in Fig. 1.
See this image and copyright information in PMC

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