Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
Sci Rep. 2024 Sep 28;14(1):22508. doi: 10.1038/s41598-024-73434-4.
Pure laparoscopic donor hepatectomy (PLDH) has become a standard practice for living donor liver transplantation in expert centers. Accurate understanding of biliary structures is crucial during PLDH to minimize the risk of complications. This study aims to develop a deep learning-based segmentation model for real-time identification of biliary structures, assisting surgeons in determining the optimal transection site during PLDH. A single-institution retrospective feasibility analysis was conducted on 30 intraoperative videos of PLDH. All videos were selected for their use of the indocyanine green near-infrared fluorescence technique to identify biliary structure. From the analysis, 10 representative frames were extracted from each video specifically during the bile duct division phase, resulting in 300 frames. These frames underwent pixel-wise annotation to identify biliary structures and the transection site. A segmentation task was then performed using a DeepLabV3+ algorithm, equipped with a ResNet50 encoder, focusing on the bile duct (BD) and anterior wall (AW) for transection. The model's performance was evaluated using the dice similarity coefficient (DSC). The model predicted biliary structures with a mean DSC of 0.728 ± 0.01 for BD and 0.429 ± 0.06 for AW. Inference was performed at a speed of 15.3 frames per second, demonstrating the feasibility of real-time recognition of anatomical structures during surgery. The deep learning-based semantic segmentation model exhibited promising performance in identifying biliary structures during PLDH. Future studies should focus on validating the clinical utility and generalizability of the model and comparing its efficacy with current gold standard practices to better evaluate its potential clinical applications.
纯腹腔镜供体肝切除术（PLDH）已成为专家中心活体供肝移植的标准实践。在 PLDH 中准确理解胆道结构对于最大限度地降低并发症风险至关重要。本研究旨在开发一种基于深度学习的分割模型，用于实时识别胆道结构，协助外科医生在 PLDH 中确定最佳的横断部位。对 30 例 PLDH 术中视频进行了单中心回顾性可行性分析。所有视频均选择使用吲哚菁绿近红外荧光技术来识别胆道结构。从分析中，从每个视频中提取了 10 个有代表性的帧，特别是在胆管分离阶段，共 300 帧。这些帧经过像素级注释以识别胆道结构和横断部位。然后使用 DeepLabV3+算法执行分割任务，该算法配备了 ResNet50 编码器，重点关注胆管（BD）和前壁（AW）的横断。使用骰子相似系数（DSC）评估模型性能。模型预测胆管结构的平均 DSC 为 0.728±0.01，AW 的平均 DSC 为 0.429±0.06。推断速度为每秒 15.3 帧，证明了手术中实时识别解剖结构的可行性。基于深度学习的语义分割模型在识别 PLDH 中的胆道结构方面表现出良好的性能。未来的研究应侧重于验证模型的临床实用性和通用性，并将其与当前金标准实践的效果进行比较，以更好地评估其潜在的临床应用。
