Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

• Published in: IEEE transactions on cybernetics Vol. 51; no. 4; pp. 2153 - 2165
• Main Authors: Xue, Jie, He, Kelei, Nie, Dong, Adeli, Ehsan, Shi, Zhenshan, Lee, Seong-Whan, Zheng, Yuanjie, Liu, Xiyu, Li, Dengwang, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Biomedical imaging; Computed tomography; Convolution; Feature maps; Image segmentation; Modules; Multitask FCN; Pancreas; pancreas segmentation; Segmentation; Shape; Skeleton; skeleton extraction; Task analysis
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2019.2955178

Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, make the segmentation task challenging. To alleviate these challenges, in this article, we propose a cascaded multitask 3-D fully convolution network (FCN) to automatically segment the pancreas. Our cascaded network is composed of two parts. The first part focuses on fast locating the region of the pancreas, and the second part uses a multitask FCN with dense connections to refine the segmentation map for fine voxel-wise segmentation. In particular, our multitask FCN with dense connections is implemented to simultaneously complete tasks of the voxel-wise segmentation and skeleton extraction from the pancreas. These two tasks are complementary, that is, the extracted skeleton provides rich information about the shape and size of the pancreas in retroperitoneum, which can boost the segmentation of pancreas. The multitask FCN is also designed to share the low- and mid-level features across the tasks. A feature consistency module is further introduced to enhance the connection and fusion of different levels of feature maps. Evaluations on two pancreas datasets demonstrate the robustness of our proposed method in correctly segmenting the pancreas in various settings. Our experimental results outperform both baseline and state-of-the-art methods. Moreover, the ablation study shows that our proposed parts/modules are critical for effective multitask learning.