Benjamin Mueller, Busisiwe Mlambo, Sue Kulason, Rogerio Nespolo, Rui Guo
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引用次数: 0
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
背景:关于评估手术里程碑的主观标准的争论,如在胆囊切除术中实现安全的关键观点(CVS),仍然是外科数据科学领域的一个突出焦点和挑战。在这项研究中,我们使用机器学习工具计算解剖指标,并研究这些客观解剖指标与实现CVS的主观标准之间的关系。方法:我们实现并校准了胆囊切除术内镜图像的零镜头单眼深度估计模型。这些深度测量与人类注释的CVS相关的三个关键解剖结构的分割掩膜相结合:囊管、囊性动脉和胆囊。然后使用计算几何技术提取结构特定的深度分布,并计算两个解剖指标:对角线长度和表面积。我们根据人工标注的CVS状态进行分组,测试了这些个案指标的显著差异。结果:2256帧(35例)按CVS标准评分,其中343帧(17例)全部符合CVS标准,384帧(17例)不符合CVS标准。标定后的深度模型在公制测量中绝对相对误差为0.063,相对平方误差为0.774。当满足所有CVS标准时,囊管和囊动脉的对角线长度和表面积均明显增大。当满足CVS标准时,囊管(囊动脉)的平均长度为7.6 mm (13.6 mm)。结论:在本研究中,我们提出了一个利用深度估计模型、解剖分割掩模和计算几何从单眼内窥镜图像生成解剖测量的管道。在满足所有CVS标准的病例中,发现囊管和囊动脉的对角线长度和表面积明显更大;这支持使用这些解剖学指标作为评估CVS成就的客观依据。
期刊介绍:
Uniquely positioned at the interface between various medical and surgical disciplines, Surgical Endoscopy serves as a focal point for the international surgical community to exchange information on practice, theory, and research.
Topics covered in the journal include:
-Surgical aspects of:
Interventional endoscopy,
Ultrasound,
Other techniques in the fields of gastroenterology, obstetrics, gynecology, and urology,
-Gastroenterologic surgery
-Thoracic surgery
-Traumatic surgery
-Orthopedic surgery
-Pediatric surgery