Intelligent segmentation and staging system for esophageal cancer based on DAEUnet and ConvNeXt networks

XIONG Lingyan; WANG Runyuan; ZHANG Fanghong; YANG You; WU Yi; WU Wei; WU Shulei

doi:10.16016/j.2097-0927.202412075

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Intelligent segmentation and staging system for esophageal cancer based on DAEUnet and ConvNeXt networks

Biomedical Engineering | 更新时间：2025-05-26

- Intelligent segmentation and staging system for esophageal cancer based on DAEUnet and ConvNeXt networks
- Journal of Army Medical University Vol. 47, Issue 10, Pages: 1135-1144(2025)
- 作者机构：
  
  1.重庆国家应用数学中心，重庆
  2.山西省肿瘤医院胸外科，山西太原
  3.重庆师范大学计算机与信息科学学院，重庆；陆军军医大学（第三军医大学）：4.生物医学工程与影像医学系数字医学教研室
  4.第一附属医院心外科，重庆
  5.Department of Cardiovascular Surgery， First Affiliated Hospital， Army Medical University （Third Military Medical University）， Chongqing， China
- 作者简介：
  
  ZHANG Fanghong， E-mail： fanghong.zhang@cqnu.edu.cn；
- 基金信息：
  
  the Scientific Research Project of Chongqing Municipal Education Commission(KJZD202200504)
- DOI：10.16016/j.2097-0927.202412075
  CLC：
- Received：13 December 2024，
  
  Revised：07 March 2025，
  
  Published：30 May 2025
- 稿件说明：
移动端阅览
熊铃焰,王润媛,张方红,等.基于DAEUnet和ConvNeXt网络的食管癌智能分割与分期诊断模型构建[J].陆军军医大学学报,2025,47(10):1135-1144.

XIONG Lingyan,WANG Runyuan,ZHANG Fanghong,et al.Intelligent segmentation and staging system for esophageal cancer based on DAEUnet and ConvNeXt networks[J].J Army Med Univ,2025,47(10):1135-1144.
熊铃焰,王润媛,张方红,等.基于DAEUnet和ConvNeXt网络的食管癌智能分割与分期诊断模型构建[J].陆军军医大学学报,2025,47(10):1135-1144. DOI：10.16016/j.2097-0927.202412075.

XIONG Lingyan,WANG Runyuan,ZHANG Fanghong,et al.Intelligent segmentation and staging system for esophageal cancer based on DAEUnet and ConvNeXt networks[J].J Army Med Univ,2025,47(10):1135-1144. DOI：10.16016/j.2097-0927.202412075.

摘要

目的

基于DAEUnet和ConvNeXt网络，通过迁移学习策略构建食管癌智能分割和T分期诊断模型。

方法

收集2018年1月至2022年4月共126例确诊为食管癌患者Dicom原始数据，其中100例来源于陆军军医大学第一附属医院胸外科，26例来自山西省肿瘤医院胸外科，数据增强后共计60 275张，搭建DAEUnet食管癌智能分割网络，并在此基础上构建ConvNeXt、Swin Transfoemr以及ResNet 3个分类网络进行食管癌T分期诊断。

结果

基于DAEUnet网络食管癌智能分割Dice相似系数（Dice similarity coefficient，DSC）为0.82，食管癌、主动脉、正常食管、纵隔淋巴结、心脏各个部位DSC分别为72.4%、87.5%、79.3%、60.5%、96.8%；在3个食管癌T分期诊断模型中，ConvNeXt模型效果最好，T1~T4期的精确度分别为0.65、0.727、0.889、0.92，曲线下面积（AUC）=0.892，优于ResNet和Swin Transformer网络。

结论

基于DAEUnet和ConvNeXt网络的食管癌智能分割与T分期诊断模型，能够提高食管癌T分期的准确性与治疗效率。

Abstract

Objective‍　‍To construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning. Methods‍　‍Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected

including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital. After data augmentation

a total of 60 275 images were obtained. The DAEUnet esophageal cancer intelligent segmentation network was built

and on this basis

3 classification networks

ConvNeXt

Swin Transformer

and ResNet were constructed for T-stage diagnosis of esophageal cancer. Results The Dice similarity coefficient (DSC) for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82

and the DSC value of the esophagus

aorta

normal esophagus

mediastinal lymph nodes

and heart was 72.4%

87.5%

79.3%

60.5% and 96.8%

respectively. Among the 3 T-stage diagnosis models for esophageal cancer

the ConvNeXt model performed the best

with a precision value for T1~T4 stages of 0.65

0.727

0.889 and 0.92

respectively

and an AUC value of 0.892

which were superior to the ResNet and Swin Transformer networks. Conclusion The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.

关键词

Keywords

references

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