Intelligent segmentation and staging system for esophageal cancer based on DAEUnet and ConvNeXt networks
10.16016/j.2097-0927.202412075
- VernacularTitle:基于DAEUnet和ConvNeXt网络的食管癌智能分割与分期诊断模型构建
- Author:
Lingyan XIONG
1
;
Runyuan WANG
;
Fanghong ZHANG
;
You YANG
;
Yi WU
;
Wei WU
;
Shulei WU
Author Information
1. 重庆国家应用数学中心
- Keywords:
esophageal cancer;
enhanced CT;
intelligent segmentation;
ConvNeXt;
T-stage diagnosis
- From:
Journal of Army Medical University
2025;47(10):1135-1144
- CountryChina
- Language:Chinese
-
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.
