Accurate assessment of 3D models of patient-specific anatomy of the liver, including underlying hepatic and biliary tree, is critical for preparation and safe execution of complex liver resections, especially due to high variability of biliary and hepatic artery anatomies. Dynamic MRI with hepatospecific contrast agents is currently the only type of diagnostic imaging that provides all anatomical information required for generation of such a model, yet there is no information in the literature on how the complete 3D model can be generated automatically. In this work, a new automated segmentation workflow for extraction of patient-specific 3D model of the liver, hepatovascular and biliary anatomy from a single multiphase MRI acquisition is developed and quantitatively evaluated. The workflow incorporates course 4D k-means clustering estimation and geodesic active contour refinement of the liver boundary, based on organ's characteristic uptake of gadolinium contrast agents overtime. Subsequently, hepatic vasculature and biliary ducts segmentations are performed using multiscale vesselness filters. The algorithm was evaluated using 15 test datasets of patients with liver malignancies of various histopathological types. It showed good correlation with expert manual segmentation, resulting in an average of 1.76 ± 2.44 mm Hausdorff distance for the liver boundary, and 0.58 ± 0.72 and 1.16 ± 1.98 mm between centrelines of biliary ducts and liver veins, respectively. A workflow for automatic segmentation of the liver, hepatic vasculature and biliary anatomy from a single diagnostic MRI acquisition was developed. This enables automated extraction of 3D models of patient-specific liver anatomy, and may facilitating better perception of organ's anatomy during preparation and execution of liver surgeries. Additionally, it may help to reduce the incidence of intraoperative biliary duct damage due to an unanticipated variation in the anatomy.

中文翻译：

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从多相MR图像自动分割肝脏表面，肝血管和胆道树解剖的工作流程。

准确评估肝脏患者特定解剖结构的3D模型（包括基础肝和胆管树）对于准备和安全执行复杂的肝切除术至关重要，尤其是由于胆管和肝动脉解剖结构的高度可变性。目前，具有肝特异性造影剂的动态MRI是唯一能够提供生成此类模型所需的所有解剖学信息的诊断成像类型，但文献中没有有关如何自动生成完整3D模型的信息。在这项工作中，开发了一种新的自动分割工作流程，用于从单个多相MRI采集中提取肝脏，肝血管和胆道解剖结构的患者特定3D模型，并对其进行了定量评估。该工作流根据器官对g造影剂的特征摄取时间的推移，结合了4D k均值聚类估计和肝脏边界的测地线主动轮廓细化。随后，使用多尺度血管过滤器进行肝血管和胆管分割。使用15种不同病理类型的肝恶性肿瘤患者的测试数据集对该算法进行了评估。它与专家手动分割显示出良好的相关性，肝边界的平均Hausdorff距离为1.76±2.44 mm，胆管和肝静脉的中心线之间的平均距离分别为0.58±0.72和1.16±1.98 mm。开发了从单个MRI诊断采集中自动分割肝脏，肝血管和胆道解剖结构的工作流程。这使得能够自动提取患者特定肝脏解剖结构的3D模型，并可能有助于在肝手术的准备和执行过程中更好地感知器官的解剖结构。另外，由于解剖结构的意外变化，它可能有助于减少术中胆管损伤的发生率。