Hemorrhaging occurs in surgeries of all types, forcing surgeons to quickly adapt to the visual interference that results from blood rapidly filling the surgical field. Introducing automation into the crucial surgical task of hemostasis management would offload mental and physical tasks from the surgeon and surgical assistants while simultaneously increasing the efficiency and safety of the operation. The first step in automation of hemostasis management is detection of blood in the surgical field. To propel the development of blood detection algorithms in surgeries, we present HemoSet, the first blood segmentation dataset based on bleeding during a live animal robotic surgery. Our dataset features vessel hemorrhage scenarios where turbulent flow leads to abnormal pooling geometries in surgical fields. These pools are formed in conditions endemic to surgical procedures -- uneven heterogeneous tissue, under glossy lighting conditions and rapid tool movement. We benchmark several state-of-the-art segmentation models and provide insight into the difficulties specific to blood detection. We intend for HemoSet to spur development of autonomous blood suction tools by providing a platform for training and refining blood segmentation models, addressing the precision needed for such robotics.

翻译：在所有类型的外科手术中，出血现象时有发生，迫使外科医生迅速适应血液快速充满手术视野所造成的视觉干扰。将自动化技术引入止血管理这一关键外科任务中，将减轻外科医生和手术助理的精神与体力负担，同时提高手术的效率和安全性。止血管理自动化的第一步是检测手术视野中的血液。为了推动手术中血液检测算法的发展，我们提出了HemoSet——首个基于活体动物机器人手术中出血情况的血液分割数据集。我们的数据集以血管出血场景为特征，其中湍流导致手术区域出现异常的血泊几何形态。这些血泊形成于外科手术特有的条件下——不均匀的异质组织表面、高光泽的照明环境以及手术器械的快速移动。我们对多种先进的分割模型进行了基准测试，并深入分析了血液检测特有的难点。我们期望HemoSet能通过为血液分割模型的训练与优化提供平台，促进自主吸血工具的开发，从而满足此类机器人技术所需的精确性。