U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images
Shuchao Chen,
Han Yang,
Jiawen Fu,
Weijian Mei,
Shuai Ren,
Yifei Liu,
Zhihua Zhu,
Lizhi Liu,
Haojiang Li,
Hongbo Chen
Affiliations
Shuchao Chen
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
Han Yang
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
Jiawen Fu
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
Weijian Mei
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
Shuai Ren
School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin, China
Yifei Liu
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
Zhihua Zhu
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
Lizhi Liu
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
Haojiang Li
State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
The effective segmentation and 3-D rendering of the esophagus and esophageal cancer from the computed tomography (CT) images can assist doctors in diagnosing esophageal cancer. Irregular and vague boundary causes great difficulty in the segmentation of esophagus and esophageal cancer. In this paper, U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice. In the new network architecture, two blocks are employed to enhance the feature extraction performance of complex abstract information, which can effectively resolve irregular and vague boundaries. A block is a skip connection operation that is similar to convolution. The architecture is trained through a dataset of 1924 slices from 10 CT scans and tested through 295 slices from 6 CT scans. The training and test datasets are expanded tenfold to simulate the segmentation of the 3-D CT image. Using the new framework, we report a 0.79 ± 0.20 dice value and 5.87 ± 9.91 Hausdorff distance. A semi-automatic scheme is then designed for the 3-D segmentation of the esophagus or esophageal cancer. The 3-D rendering of the esophagus or esophageal cancer is implemented to assist in the diagnosis of esophageal cancer.
