Gastric cancer segmentation and T staging algorithm based on convolutional neural network(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第2期
- Page:
- 215-223
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Gastric cancer segmentation and T staging algorithm based on convolutional neural network
- Author(s):
- ZHOU Yilong1; WEI Ziran1; CAI Qingping2; GAO Yongbin2; MA Shuo2
- 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201600, China 2. Second Department of General Surgery, Shanghai Changzheng Hospital, Shanghai 200003, China
- Keywords:
- Keywords: convolutional neural network gastric cancer segmentation T staging attention mechanism multi-core residual densely connected dilated convolution
- PACS:
- R318；TP391.41
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.02.015
- Abstract:
- Abstract: The accurate segmentation of gastric cancer and the accurate prediction of gastric wall tumor invasion depth based on gastric cancer CT images are essential for screening of gastric diseases, clinical diagnosis, preoperative prediction and postoperative evaluation planning. A gastric cancer segmentation and T staging algorithm based on convolutional neural network (SC-Net) is proposed to accurately segment gastric cancer in CT images and to qualitatively stage the tumors. SC-Net has two main lines, namely segmentation main line and classification main line. For the novel algorithm, the training is divided into two steps. The first step is to train only the main line of segmentation for obtaining the rough segmentation result, and then on the basis of the first step, the main lines of segmentation and classification are jointly trained to obtain the final fine segmentation and T staging results. In order to improve the algorithms attention to the gastric cancer region, an attention mechanism is proposed to enhance the accuracy of the algorithm. In addition, multi-core residual module and densely connected dilated convolution module are used to extract underlying characteristic information. The qualitative and quantitative analyses on the proposed algorithm show that the proposed method is superior to similar methods in gastric cancer segmentation and T staging. The proposed method has the potential to screen stomach diseases and assist doctors in diagnosis.
Last Update: 2022-03-07