应用于 ERCP 的 3D Slicer 胆管分割方法：优点与缺点
Bile Duct Segmentation Methods Under 3D Slicer Applied to ERCP: Advantages and Disadvantages
AI总结：
该研究在 3D Slicer 中基于 10 例患者比较阈值法、泛洪填充法和区域生长法的胆道分割效果，指出自动区域生长法更快但质量较低，并建议探索深度学习。
摘要：
本文介绍了对用于三维重建的胆道分割方法的评估；通过使用 3D Slicer 软件，这类重建可能在各种关键介入操作中非常有用，例如内镜逆行胰胆管造影（ERCP）。本文评估了用于三维重建的胆道分割技术，这些技术借助 3D Slicer 软件，可在多种关键手术（如 ERCP）中被证明具有很高价值。研究从优点和缺点方面评估了三种不同方法，即阈值法、泛洪填充法和区域生长法。研究纳入 10 例患者病例，并采用定量指标和定性评价，将不同分割方法得到的分割结果与真值进行比较。结果表明，阈值法几乎是手动的且耗时，而泛洪填充法是半自动的，同样耗时。尽管两种方法都能提高分割质量，但它们不可复现。因此，研究开发了一种基于区域生长的自动方法，以缩短分割时间，但代价是质量有所下降。这些发现突出了不同传统分割方法的优缺点，并强调需要探索替代方法，例如深度学习，以在 ERCP 场景下优化胆道分割。
英文摘要：
This article presents an evaluation of biliary tract segmentation methods used for 3D reconstruction, which may be very usefull in various critical interventions, such as endoscopic retrograde cholangiopancreatography (ERCP), using the 3D Slicer software. This article provides an assessment of biliary tract segmentation techniques employed for 3D reconstruction, which can prove highly valuable in diverse critical procedures like endoscopic retrograde cholangiopancreatography (ERCP) through the utilization of 3D Slicer software. Three different methods, namely thresholding, flood filling, and region growing, were assessed in terms of their advantages and disadvantages. The study involved 10 patient cases and employed quantitative indices and qualitative evaluation to assess the segmentations obtained by the different segmentation methods against ground truth. The results indicate that the thresholding method is almost manual and time-consuming, while the flood filling method is semi-automatic and also time-consuming. Although both methods improve segmentation quality, they are not reproducible. Therefore, an automatic method based on region growing was developed to reduce segmentation time, albeit at the expense of quality. These findings highlight the pros and cons of different conventional segmentation methods and underscore the need to explore alternative approaches, such as deep learning, to optimize biliary tract segmentation in the context of ERCP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
