基于超声导航切除的结直肠癌肝转移自动分割
Automatic segmentation of colorectal liver metastases for ultrasound-based navigated resection
AI总结：
本文提出基于裁剪区域训练的三维U-Net，对术中超声中的结直肠癌肝转移进行自动分割，在极少人工输入下实现近实时、无需配准的手术导航，精度接近专家水平并显著缩短操作时间。
摘要：
引言：结直肠癌肝转移（CRLM）的术中精确勾画对于实现阴性切缘至关重要，但由于术中超声（iUS）对比度低、噪声大且依赖操作者，这一任务仍具挑战性。自动分割可提升基于超声的导航工作流程的精度与效率。方法：使用来自85例CRLM患者的85个追踪三维iUS体数据训练并评估一个通过nnU-Net框架实现的三维U-Net。比较了两种变体：一种在完整iUS体数据上训练，另一种在肿瘤周围的裁剪区域上训练。在回顾性和前瞻性数据集上，采用Dice相似系数（DSC）、豪斯多夫距离（HDist.）和相对体积差（RVD）评估分割精度。该工作流程已集成到3D Slicer中，用于术中实时使用。结果：裁剪体数据模型在所有指标上均显著优于完整体数据模型（AUC-ROC = 0.898 vs 0.718）。其达到中位DSC = 0.74、召回率 = 0.79、HDist. = 17.1 mm，与半自动分割相当，但执行速度约快4倍（约1分钟）。前瞻性术中测试证实了稳健且一致的表现，具有临床可接受的实时手术引导精度。结论：使用裁剪三维U-Net对iUS中的CRLM进行自动三维分割，可在极少操作者输入的情况下提供可靠、近实时的结果。该方法可实现高效、无需配准的基于超声的肝脏手术导航，接近专家级精度，同时大幅减少人工工作量和手术时间。
英文摘要：
Introduction: Accurate intraoperative delineation of colorectal liver metastases (CRLM) is crucial for achieving negative resection margins but remains challenging using intraoperative ultrasound (iUS) due to low contrast, noise, and operator dependency. Automated segmentation could enhance precision and efficiency in ultrasound-based navigation workflows. Methods: Eighty-five tracked 3D iUS volumes from 85 CRLM patients were used to train and evaluate a 3D U-Net implemented via the nnU-Net framework. Two variants were compared: one trained on full iUS volumes and another on cropped regions around tumors. Segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), Hausdorff Distance (HDist.), and Relative Volume Difference (RVD) on retrospective and prospective datasets. The workflow was integrated into 3D Slicer for real-time intraoperative use. Results: The cropped-volume model significantly outperformed the full-volume model across all metrics (AUC-ROC = 0.898 vs 0.718). It achieved median DSC = 0.74, recall = 0.79, and HDist. = 17.1 mm comparable to semi-automatic segmentation but with ~4x faster execution (~ 1 min). Prospective intraoperative testing confirmed robust and consistent performance, with clinically acceptable accuracy for real-time surgical guidance. Conclusion: Automatic 3D segmentation of CRLM in iUS using a cropped 3D U-Net provides reliable, near real-time results with minimal operator input. The method enables efficient, registration-free ultrasound-based navigation for hepatic surgery, approaching expert-level accuracy while substantially reducing manual workload and procedure time.
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。
