Sarkar Sahar, Rahmani Mahdiyeh, Farnia Parastoo, Ahmadian Alireza, Mozayani Nasser
School of Computer Engineering, Artificial Intelligence & Robotics Department, Iran University of Science and Technology, Tehran, Iran.
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Phys Eng Sci Med. 2025 Jul 14. doi: 10.1007/s13246-025-01581-7.
Accurate liver vessel segmentation is essential for effective liver surgery pre-planning, and reducing surgical risks since it enables the precise localization and extensive assessment of complex vessel structures. Manual liver vessel segmentation is a time-intensive process reliant on operator expertise and skill. The complex, tree-like architecture of hepatic and portal veins, which are interwoven and anatomically variable, further complicates this challenge. This study addresses these challenges by proposing the UNETR (U-Net Transformers) architecture for the multi-class segmentation of portal and hepatic veins in liver CT images. UNETR leverages a transformer-based encoder to effectively capture long-range dependencies, overcoming the limitations of convolutional neural networks (CNNs) in handling complex anatomical structures. The proposed method was evaluated on contrast-enhanced CT images from the IRCAD as well as a locally dataset developed from a hospital. On the local dataset, the UNETR model achieved Dice coefficients of 49.71% for portal veins, 69.39% for hepatic veins, and 76.74% for overall vessel segmentation, while reaching Dice coefficients of 62.54% for vessel segmentation on the IRCAD dataset. These results highlight the method's effectiveness in identifying complex vessel structures across diverse datasets. These findings underscore the critical role of advanced architectures and precise annotations in improving segmentation accuracy. This work provides a foundation for future advancements in automated liver surgery pre-planning, with the potential to enhance clinical outcomes significantly. The implementation code is available on GitHub: https://github.com/saharsarkar/Multiclass-Vessel-Segmentation .
准确的肝脏血管分割对于有效的肝脏手术预规划和降低手术风险至关重要，因为它能够对复杂的血管结构进行精确的定位和全面的评估。手动肝脏血管分割是一个耗时的过程，依赖于操作人员的专业知识和技能。肝静脉和门静脉复杂的树状结构相互交织且解剖结构多变，这进一步加剧了这一挑战。本研究通过提出用于肝脏CT图像中门静脉和肝静脉多类分割的UNETR（U-Net Transformers）架构来应对这些挑战。UNETR利用基于Transformer的编码器有效地捕捉长距离依赖关系，克服了卷积神经网络（CNN）在处理复杂解剖结构方面的局限性。所提出的方法在来自IRCAD的增强CT图像以及从一家医院开发的本地数据集上进行了评估。在本地数据集上，UNETR模型对于门静脉的Dice系数为49.71%，对于肝静脉为69.39%，对于整体血管分割为76.74%，而在IRCAD数据集上血管分割的Dice系数达到62.54%。这些结果凸显了该方法在跨不同数据集识别复杂血管结构方面的有效性。这些发现强调了先进架构和精确标注在提高分割准确性方面的关键作用。这项工作为未来自动化肝脏手术预规划的进展奠定了基础，有可能显著提高临床结果。实现代码可在GitHub上获取：https://github.com/saharsarkar/Multiclass-Vessel-Segmentation 。
