Abstract：
Appropriate needle manipulation to avoid abrupt deformation of fragile vessels is a critical determinant of the success of microvascular anastomosis. However, no study has yet evaluated the area changes in surgical objects using surgical videos. The present study therefore aimed to develop a deep learning-based semantic segmentation algorithm to assess the area change of vessels during microvascular anastomosis for objective surgical skill assessment with regard to the \"respect for tissue.\" The semantic segmentation algorithm was trained based on a ResNet-50 network using microvascular end-to-side anastomosis training videos with artificial blood vessels. Using the created model, video parameters during a single stitch completion task, including the coefficient of variation of vessel area (CV-VA), relative change in vessel area per unit time (ΔVA), and the number of tissue deformation errors (TDE), as defined by a ΔVA threshold, were compared between expert and novice surgeons. A high validation accuracy (99.1%) and Intersection over Union (0.93) were obtained for the auto-segmentation model. During the single-stitch task, the expert surgeons displayed lower values of CV-VA (p < 0.05) and ΔVA (p < 0.05). Additionally, experts committed significantly fewer TDEs than novices (p < 0.05), and completed the task in a shorter time (p < 0.01). Receiver operating curve analyses indicated relatively strong discriminative capabilities for each video parameter and task completion time, while the combined use of the task completion time and video parameters demonstrated complete discriminative power between experts and novices. In conclusion, the assessment of changes in the vessel area during microvascular anastomosis using a deep learning-based semantic segmentation algorithm is presented as a novel concept for evaluating microsurgical performance. This will be useful in future computer-aided devices to enhance surgical education and patient safety.
摘要：
适当的针头操作以避免脆弱血管的突然变形是微血管吻合成功的关键决定因素。然而,尚未有研究使用手术录像评估手术对象的面积变化.因此，本研究旨在开发一种基于深度学习的语义分割算法，以评估微血管吻合过程中血管的面积变化，以客观地评估对组织的尊重。“语义分割算法是基于ResNet-50网络使用具有人造血管的微血管端到端吻合训练视频进行训练的。使用创建的模型，在单个缝合完成任务期间的视频参数，包括血管面积变异系数(CV-VA)，单位时间内血管面积的相对变化(ΔVA)，和组织变形误差(TDE)的数量，由ΔVA阈值定义，在专家和新手外科医生之间进行了比较。对于自动分割模型，获得了较高的验证准确性（99.1％）和联合交集（0.93）。在单针任务中，专家外科医生显示较低的CV-VA值(p<0.05)和ΔVA值(p<0.05)。此外,专家承诺的TDE明显少于新手(p<0.05)，并在较短的时间内完成任务(p<0.01)。接收器工作曲线分析表明，每个视频参数和任务完成时间具有相对较强的辨别能力，而任务完成时间和视频参数的结合使用显示了专家和新手之间的完全区分能力。总之,使用基于深度学习的语义分割算法评估微血管吻合过程中血管面积的变化被提出作为评估显微外科手术性能的新概念。这将在未来的计算机辅助设备中有用，以增强手术教育和患者安全。
