文献类型：期刊文献

英文题名：Abdominal multi-organ segmentation on 3D negative-contrast CT cholangiopancreatography: a comparative study of deep learning methods

作者：Li, Bin[1];Zhang, Jian[2];Fu, Hao[2];Zhang, Lei[1];Chen, Fang Ming[1];Zhao, Wei[3,4];Zou, Jun Zhong[5];Zou, Hao[3,5];Zhang, Zhui Yang[1]

机构：[1]Jiangnan Univ, Wuxi Peoples Hosp 2, Med Ctr, Dept Radiol, Wuxi, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Dept Automat, Shanghai, Peoples R China;[3]Tsinghua Univ Shenzhen, Res Inst, Ctr Intelligent Med Imaging & Hlth, Shenzhen, Peoples R China;[4]Tsimage Med Technol Shenzhen Co Ltd, Shenzhen, Peoples R China;[5]Suumi Res Inst, Atami, Japan

年份：2026

卷号：51

期号：6

起止页码：2857

外文期刊名：ABDOMINAL RADIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001616711800001)】；

基金：This study was funded by "Wuxi Taihu Lake Talent Plan, Leading Talents in Medical and Health Profession (2020)".

语种：英文

外文关键词：Multi-organ segmentation; Abdominal; Deep learning; Negative-contrast CT cholangiopancreatography; Three-dimensional; Pancreatobiliary system

摘要：ObjectivesThis study aims to automate segmentation of the biliary and pancreatic systems on 3D negative-contrast CT cholangiopancreatography (3D-nCTCP) to improve preoperative planning and diagnosis.MethodsWe retrospectively collected dual-phase enhanced CT data from 111 patients with malignant low biliary obstruction. Portal phase data were processed and annotated by an expert radiologist. The dataset, comprising 25,700 images, was split into 91 patients for training/validation and 20 patients for testing. Four state-of-the-art segmentation models, namely TransUNet 2D, nnU-Net 2D, Swin-UNETR 2D, and Swin-UNETR 3D, were implemented and quantitatively compared. Model performance was evaluated using the Dice Similarity Coefficient (DSC) and the Average Symmetric Surface Distance (ASSD), with the inter-observer variability (IOV) serving as the clinical benchmark.ResultsAcross all models, segmentation performance exhibited high accuracy for the liver, with notably lower accuracy for smaller, more complex organs (duodenum, pancreas, biliary system). The Swin-UNETR 3D model demonstrated superior overall segmentation performance, particularly for challenging organs, with competitive stability. Swin-UNETR 3D achieved a mean DSC of 96.12% +/- 1.09% and ASSD of 4.60 mm +/- 8.25 mm for the liver, mean DSC of 75.31% +/- 11.31% and ASSD of 4.42 mm +/- 5.84 mm for the duodenum, mean DSC of 81.00% +/- 6.33% and ASSD of 2.07 mm +/- 1.70 mm for the pancreas, and mean DSC of 88.64% +/- 6.74% and ASSD of 5.47 mm +/- 12.13 mm for the biliary system. The 3D volumetric approach in Swin-UNETR 3D outperformed its 2D counterparts (TransUNet 2D, nnU-Net 2D, Swin-UNETR 2D) in most metrics, particularly for the duodenum, where it achieved the highest mean DSC and improved boundary localization compared to 2D models.ConclusionThe comparative analysis demonstrates the superiority of 3D volumetric models (Swin-UNETR 3D) over 2D models for accurate and stable abdominal multi-organ segmentation on 3D-nCTCP, reduces manual annotation time, and may aid broader clinical adoption.