HemoSet: The First Blood Segmentation Dataset for Automation of
  Hemostasis Management
Albert J. Miao Shan Lin Jingpei Lu Florian Richter Benjamin Ostrander Emily K. Funk Ryan K. Orosco Michael C. Yip
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.