Fully automatic bile duct segmentation in magnetic resonance cholangiopancreatography for biliary surgery planning using deep learning

IF 3.3 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-09-15 DOI:10.1016/j.ejrad.2025.112415

Haisu Tao , Junfeng Wang , Kangwei Guo , Wang Luo , Xiaojun Zeng , Mingjun Lu , Jinyu Lin , Baihong Li , Yinling Qian , Jian Yang

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引用次数: 0

Abstract

Objectives

To automatically and accurately perform three-dimensional reconstruction of dilated and non-dilated bile ducts based on magnetic resonance cholangiopancreatography (MRCP) data, assisting in the formulation of optimal surgical plans and guiding precise bile duct surgery.

Methods

A total of 249 consecutive patients who underwent standardized 3D-MRCP scans were randomly divided into a training cohort (n = 208) and a testing cohort (n = 41). Ground truth segmentation was manually delineated by two hepatobiliary surgeons or radiologists following industry certification procedures and reviewed by two expert-level physicians for biliary surgery planning. The deep learning semantic segmentation model was constructed using the nnU-Net framework. Model performance was assessed by comparing model predictions with ground truth segmentation as well as real surgical scenarios. The generalization of the model was tested on a dataset of 10 3D-MRCP scans from other centers, with ground truth segmentation of biliary structures.

Results

The evaluation was performed on 41 internal test sets and 10 external test sets, with mean Dice Similarity Coefficient (DSC) values of respectively 0.9403 and 0.9070. The correlation coefficient between the 3D model based on automatic segmentation predictions and the ground truth results exceeded 0.95. The 95 % limits of agreement (LoA) for biliary tract length ranged from −4.456 to 4.781, and for biliary tract volume ranged from −3.404 to 3.650 ml. Furthermore, the intraoperative Indocyanine green (ICG) fluorescence imaging and operation situation validated that this model can accurately reconstruct biliary landmarks.

Conclusion

By leveraging a deep learning algorithmic framework, an AI model can be trained to perform automatic and accurate 3D reconstructions of non-dilated bile ducts, thereby providing guidance for the preoperative planning of complex biliary surgeries.

查看原文本刊更多论文

磁共振胆管造影中的全自动胆管分割，用于胆道手术计划的深度学习。

目的：基于磁共振胆管胰胆管造影（MRCP）数据，自动准确地对扩张及非扩张胆管进行三维重建，协助制定最佳手术方案，指导胆管精准手术。方法：共有249例连续接受标准化3D-MRCP扫描的患者被随机分为训练组（n = 208）和测试组（n = 41）。根据行业认证程序，由两名肝胆外科医生或放射科医生手动划分，并由两名专家级医生对胆道手术计划进行审查。采用nnU-Net框架构建深度学习语义分割模型。通过比较模型预测与真实分割以及真实手术场景来评估模型性能。模型的泛化在其他中心的10个3D-MRCP扫描数据集上进行了测试，并对胆道结构进行了地面真值分割。结果：对41个内部测试集和10个外部测试集进行了评价，Dice Similarity Coefficient （DSC）的平均值分别为0.9403和0.9070。基于自动分割预测的三维模型与地面真实度结果的相关系数大于0.95。胆道长度的95%一致性限（LoA）范围为-4.456 ~ 4.781，胆道容积的95%一致性限（LoA）范围为-3.404 ~ 3.650 ml。此外，术中吲哚菁绿（ICG）荧光成像和手术情况验证了该模型能够准确重建胆道标志。结论：利用深度学习算法框架，可以训练人工智能模型对未扩张胆管进行自动准确的三维重建，从而为复杂胆道手术的术前规划提供指导。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.