Mueller Benjamin, Mlambo Busisiwe, Kulason Sue, Nespolo Rogerio, Guo Rui
Digital Solutions, Intuitive Surgical Inc., 5655 Spalding Drive, Peachtree Corners, GA, 30092, USA.
Surg Endosc. 2025 Sep 12. doi: 10.1007/s00464-025-12137-x.
Debates on the subjective criteria for evaluating Surgical Milestones, such as achieving the critical view of safety (CVS) during cholecystectomy, remain a prominent focus and challenge in the field of surgical data science. In this study, we computed anatomical metrics with machine learning tools and investigated the relationship between these objective anatomical metrics and subjective criteria for CVS achievement.
We implemented and calibrated a zero-shot monocular depth estimation model for endoscopic images from cholecystectomies. These depth measures were integrated with human-annotated segmentation masks of three key anatomical structures relevant to CVS: cystic duct, cystic artery, and gallbladder. Computational geometry techniques were then employed to extract structure-specific depth distributions and compute two anatomical metrics: diagonal length and surface area. We tested for significant differences in these case-wise metrics, grouped by human-annotated CVS status.
2256 frames (35 cases) were graded on CVS criteria, of which 343 frames (17 cases) met all three CVS criteria and 384 frames (17 cases) met no CVS criteria. The calibrated depth model achieves 0.063 on absolute relative error and 0.774 on squared relative error in the metric measurement. The diagonal length and surface area of both the cystic duct and cystic artery were significantly larger when all CVS criteria were met. On average, the cystic duct (cystic artery) length was 7.6 mm (13.6 mm) longer when CVS criteria was met.
In this study, we presented a pipeline that generates anatomical measures from monocular endoscopic images utilizing a depth estimation model, anatomical segmentation masks, and computational geometry. The diagonal length and surface area of the cystic duct and cystic artery were found to be significantly larger in cases where all CVS criteria are met; this lends support to the use of these anatomical metrics as objective grounds for assessing CVS achievement.
关于评估手术里程碑的主观标准的争论,例如在胆囊切除术中实现安全关键视野(CVS),仍然是手术数据科学领域的一个突出焦点和挑战。在本研究中,我们使用机器学习工具计算解剖学指标,并研究这些客观解剖学指标与实现CVS的主观标准之间的关系。
我们为胆囊切除术的内镜图像实现并校准了一个零样本单目深度估计模型。这些深度测量值与人类标注的与CVS相关的三个关键解剖结构的分割掩码相结合:胆囊管、胆囊动脉和胆囊。然后采用计算几何技术提取特定结构的深度分布,并计算两个解剖学指标:对角线长度和表面积。我们测试了按人类标注的CVS状态分组的这些逐例指标的显著差异。
根据CVS标准对2256帧(35例)进行了分级,其中343帧(17例)符合所有三项CVS标准,384帧(17例)未符合任何CVS标准。校准后的深度模型在度量测量中的绝对相对误差为0.063,平方相对误差为0.774。当所有CVS标准都满足时,胆囊管和胆囊动脉的对角线长度和表面积均显著更大。平均而言,当满足CVS标准时,胆囊管(胆囊动脉)长度长7.6毫米(13.6毫米)。
在本研究中,我们提出了一种流程,该流程利用深度估计模型、解剖分割掩码和计算几何从单目内镜图像生成解剖学测量值。发现在所有CVS标准都满足的情况下,胆囊管和胆囊动脉的对角线长度和表面积显著更大;这支持将这些解剖学指标用作评估CVS实现情况的客观依据。