用深度学习自动分割肝胆MRI，助力术前精准规划。
Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
- 基于nnU-Net模型，自动分割肝脏、血管和肿瘤等解剖结构。
- 对肝脏实质分割准确率高达97%，肿瘤检测率达76.6%。
- 临床验证显示只需少量修正，适合推广至常规术前评估。
本研究旨在开发并评估一种基于深度学习的自动化分割方法，用于钆塞酸增强MRI肝胆期图像中肝实质、肿瘤、门静脉、肝静脉及胆管树的解剖结构分割，以简化术前规划的临床流程。在2020年1月至2023年10月期间接受肝手术的90例患者中，由人工完成肝胆期MRI的标注。使用nnU-Net v1模型在72例患者数据上训练，重点优化细小结构与形态保持。在18例患者的测试集上，通过骰子相似系数（DSC）对比自动与人工分割结果进行评估。临床集成后，另生成10例用于评估的分割结果，经人工修正后用于临床，统计调整程度。测试集结果显示：肝实质DSC为0.97±0.01，肝静脉0.80±0.04，胆管树0.79±0.07，肿瘤0.77±0.17，门静脉0.74±0.06；平均肿瘤检出率为76.6±24.1%，每例患者平均漏检1个假阳性。评估数据集显示临床使用仅需微调，三维模型的肝实质（1.00±0.00）、门静脉（0.98±0.01）和肝静脉（0.95±0.07）分割精度高；肿瘤分割变异较大（DSC 0.80±0.27）。前瞻性临床应用中，模型发现3个放射科医生最初遗漏的肿瘤。结论：该nnU-Net方法可实现肝部解剖结构的高精度、自动化分割，推动3D术前规划成为所有肝手术患者的标准化流程。
原文摘要 · Abstract (English)
Background: The aim of this study was to develop and evaluate a deep learning-based automated segmentation method for hepatic anatomy (i.e., parenchyma, tumors, portal vein, hepatic vein and biliary tree) from the hepatobiliary phase of gadoxetic acid-enhanced MRI. This method should ease the clinical workflow of preoperative planning. Methods: Manual segmentation was performed on hepatobiliary phase MRI scans from 90 consecutive patients who underwent liver surgery between January 2020 and October 2023. A deep learning network (nnU-Net v1) was trained on 72 patients with an extra focus on thin structures and topography preservation. Performance was evaluated on an 18-patient test set by comparing automated and manual segmentations using Dice similarity coefficient (DSC). Following clinical integration, 10 segmentations (assessment dataset) were generated using the network and manually refined for clinical use to quantify required adjustments using DSC. Results: In the test set, DSCs were 0.97+/-0.01 for liver parenchyma, 0.80+/-0.04 for hepatic vein, 0.79+/-0.07 for biliary tree, 0.77+/-0.17 for tumors, and 0.74+/-0.06 for portal vein. Average tumor detection rate was 76.6+/-24.1%, with a median of one false-positive per patient. The assessment dataset showed minor adjustments were required for clinical use of the 3D models, with high DSCs for parenchyma (1.00+/-0.00), portal vein (0.98+/-0.01) and hepatic vein (0.95+/-0.07). Tumor segmentation exhibited greater variability (DSC 0.80+/-0.27). During prospective clinical use, the model detected three additional tumors initially missed by radiologists. Conclusions: The proposed nnU-Net-based segmentation method enables accurate and automated delineation of hepatic anatomy. This enables 3D planning to be applied efficiently as a standard-of-care for every patient undergoing liver surgery.
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