[1]周意龙,卫子然,蔡清萍,等.基于卷积神经网络胃癌分割与T分期算法[J].中国医学物理学杂志,2022,39(2):215-223.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.015]
　ZHOU Yilong,WEI Ziran,CAI Qingping,et al.Gastric cancer segmentation and T staging algorithm based on convolutional neural network[J].Chinese Journal of Medical Physics,2022,39(2):215-223.[doi:DOI:10.3969/j.issn.1005-202X.2022.02.015]
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基于卷积神经网络胃癌分割与T分期算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第2期
页码:
215-223
栏目:
医学影像物理
出版日期:
2022-02-26

文章信息/Info

Title:
Gastric cancer segmentation and T staging algorithm based on convolutional neural network
文章编号:
1005-202X（2022）02-0215-09
作者:
周意龙1; 卫子然1; 蔡清萍2; 高永彬2; 马硕2
1.上海工程技术大学电子电气工程学院， 上海 201600； 2.上海长征医院普外二科， 上海 200003
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
关键词:
卷积神经网络; 胃癌; 分割; T分期; 注意力机制; 多核残差; 密集空洞卷积
Keywords:
Keywords: convolutional neural network gastric cancer segmentation T staging attention mechanism multi-core residual densely connected dilated convolution
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2022.02.015
文献标志码:
A
摘要:
基于胃癌CT图像准确分割胃癌和精准预测胃壁肿瘤浸润深度对于筛查胃部疾病、临床诊断、术前预测、术后评估计划至关重要。为了准确地从胃癌CT图像分割出胃癌并对肿瘤进行定性分期，提出一种基于卷积神经网络的胃癌分割与T分期算法（SC-Net）。SC-Net有两条主干线：分割主线、分类主线。这种新型算法分为两步进行训练：第一步只训练分割主线得到肿瘤的粗分割结果，然后在第一步基础之上联合训练分割分类主线得到最终的精分割和肿瘤T分期结果。为了提高算法对胃癌区域的关注度，提出了注意力机制加强算法的准确性。此外还使用多核残差模块和密集连接空洞卷积模块提取深层的特征信息。对所提算法进行定性定量分析。实验表明所提方法在胃癌分割和T分期上均优于同类方法，所提方法有作为筛查胃部疾病、辅助医生诊断的潜力。
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.

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备注/Memo

备注/Memo:
【收稿日期】2021-09-12 【基金项目】上海市自然科学基金（18411952800） 【作者简介】周意龙，硕士，研究方向：计算机视觉、图像处理，E-mail: 154390388@qq.com 【通信作者】卫子然，博士，副教授，E-mail: weiziran0010185@163.com
更新日期/Last Update: 2022-03-07