U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer in Computed Tomography Images

Shuchao Chen,Lizhi Liu,Haojiang Li,Weijian Mei,Zhihua Zhu,Shuai Ren,Hongbo Chen,Han Yang,Yifei Liu,Jiawen Fu

doi:10.1109/access.2019.2923760

Abstract

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.

Highlights

Eastern Asia and Eastern and Southern Africa show a high rate of esophageal cancer [1]
Computed tomography (CT) is a very important diagnostic technique for esophageal cancer because computed tomography (CT) images of chest, abdomen, and pelvis can be used to evaluate tumor metastasis to adjacent tissues or distant organs, such as liver and lymph nodes; this information is important for the diagnosis and prognosis of esophageal cancer [3], [4]
SEMI-AUTOMATIC 3-D SEGMENTATION In the previous section, we proposed a method to segment esophageal or esophageal cancer in 2-D CT images

Summary

INTRODUCTION

Eastern Asia and Eastern and Southern Africa show a high rate of esophageal cancer [1]. S. Chen et al.: U-Net Plus: Deep Semantic Segmentation for Esophagus and Esophageal Cancer. U-Net has been developed on the basis of FCN and considers skip connection between encoder and decoder This process can effectively combine the features from shallow and deep layers through multipath confusion, which solves the spatial loss of feature map and improves the accuracy of semantic segmentation. The network architecture of two encoder–decoder blocks with skip connection is adopted to improve the capability of complex abstract information processing. This characteristic helps improve the segmentation capability of irregular and vague boundary of esophageal or esophageal cancer. A semi-automatic scheme is designed to segment the esophagus or esophageal cancer from 3-D CT images. 3-D rendering esophagus or esophageal cancer will help doctors diagnose esophageal cancer

PROPOSED METHOD

EVALUATION METRIC

RESULTS AND ANALYSIS

CONCLUSION