标题： 基于nnU-Net的自动手术规划：肝胆期MRI中的解剖结构勾画 显示英文标题

标题： Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI

摘要： 背景：本研究的目的是开发并评估一种基于深度学习的自动化分割方法，用于从钆塞酸增强MRI的肝胆期图像中分割肝部解剖结构（即实质、肿瘤、门静脉、肝静脉和胆管树）。该方法应能简化术前规划的临床流程。 方法：对2020年1月至2023年10月期间接受肝脏手术的90例连续患者的肝胆期MRI扫描进行了手动分割。一个深度学习网络（nnU-Net v1）在72名患者的数据上进行训练，特别关注细小结构和拓扑结构的保持。通过比较自动分割和手动分割的Dice相似性系数（DSC）对18名患者的测试集进行性能评估。在临床集成后，使用网络生成了10个分割结果（评估数据集），并进行了手动修正以用于临床，以使用DSC量化所需的调整。 结果：在测试集中，DSC分别为肝实质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。平均肿瘤检测率为76.6+/-24.1%，每位患者平均有一个假阳性。评估数据集显示，为了临床使用3D模型需要少量调整，实质（1.00+/-0.00）、门静脉（0.98+/-0.01）和肝静脉（0.95+/-0.07）的DSC较高。肿瘤分割表现出更大的变异性（DSC 0.80+/-0.27）。在前瞻性临床使用中，该模型检测到三个最初被放射科医生遗漏的肿瘤。 结论：所提出的基于nnU-Net的分割方法能够准确且自动地勾画肝部解剖结构。这使得3D规划能够高效地作为每个接受肝脏手术患者的常规护理标准。 显示英文摘要

摘要： 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.