Cholecystectomy (gallbladder removal) is one of the most common procedures in the US, with more than 1.2M procedures annually. Compared with classical open cholecystectomy, laparoscopic cholecystectomy (LC) is associated with significantly shorter recovery period, and hence is the preferred method. However, LC is also associated with an increase in bile duct injuries (BDIs), resulting in significant morbidity and mortality. The primary cause of BDIs from LCs is misidentification of the cystic duct with the bile duct. Critical view of safety (CVS) is the most effective of safety protocols, which is said to be achieved during the surgery if certain criteria are met. However, due to suboptimal understanding and implementation of CVS, the BDI rates have remained stable over the last three decades. In this paper, we develop deep-learning techniques to automate the assessment of CVS in LCs. An innovative aspect of our research is on developing specialized learning techniques by incorporating domain knowledge to compensate for the limited training data available in practice. In particular, our CVS assessment process involves a fusion of two segmentation maps followed by an estimation of a certain region of interest based on anatomical structures close to the gallbladder, and then finally determination of each of the three CVS criteria via rule-based assessment of structural information. We achieved a gain of over 11.8% in mIoU on relevant classes with our two-stream semantic segmentation approach when compared to a single-model baseline, and 1.84% in mIoU with our proposed Sobel loss function when compared to a Transformer-based baseline model. For CVS criteria, we achieved up to 16% improvement and, for the overall CVS assessment, we achieved 5% improvement in balanced accuracy compared to DeepCVS under the same experiment settings.

翻译：胆囊切除术（胆囊摘除）是美国最常见的手术之一，每年超过120万例。与传统的开腹胆囊切除术相比，腹腔镜胆囊切除术（LC）恢复期显著缩短，因此成为首选方法。然而，LC也伴随着胆管损伤（BDI）的增加，导致显著的发病率和死亡率。LC中BDI的主要原因是将胆囊管误认为胆管。关键安全视图（CVS）是最有效的安全协议，如果满足特定标准，则被认为在手术过程中实现。然而，由于对CVS的理解和执行不够理想，过去三十年来BDI发生率保持稳定。本文开发了深度学习技术以自动化评估LC中的CVS。我们研究的一个创新之处在于通过整合领域知识开发专门的学习技术，以弥补实践中有限训练数据的不足。具体而言，我们的CVS评估过程包括融合两种分割图，然后根据胆囊附近的解剖结构估计特定感兴趣区域，最后通过基于规则的结构信息评估确定三个CVS标准中的每一个。与单模型基线相比，我们的双流语义分割方法在相关类别上的mIoU提升了11.8%以上；与基于Transformer的基线模型相比，我们提出的Sobel损失函数使mIoU提升了1.84%。对于CVS标准，我们实现了高达16%的提升；对于整体CVS评估，在相同实验设置下，与DeepCVS相比，我们的平衡准确率提升了5%。