HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management
This addresses the need for automation in surgical hemostasis to improve safety and efficiency, though it is incremental as it focuses on dataset creation rather than a new method.
The authors tackled the problem of automating hemostasis management in surgery by creating HemoSet, the first blood segmentation dataset from live animal robotic surgeries, and benchmarked state-of-the-art models to highlight specific challenges in blood detection.
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.