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HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management
Image and Video Processing · Submitted 24 Mar 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | 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. | background 0.93 | 0.06 | 0.11 | asserted 0.58 |
| s2 | The first step in automation of hemostasis management is detection of blood in the surgical field. | background 0.98 | 0.07 | 0.05 | asserted 1.00 |
| s3 | 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. | contribution 1.00 | 0.93 | 0.21 | asserted 1.00 |
| s4 | Our dataset features vessel hemorrhage scenarios where turbulent flow leads to abnormal pooling geometries in surgical fields. | contribution 0.61 | 0.79 | 0.04 | asserted 1.00 |
| s5 | These pools are formed in conditions endemic to surgical procedures -- uneven heterogeneous tissue, under glossy lighting conditions and rapid tool movement. | method 0.38 | 0.50 | 0.03 | asserted 1.00 |
| s6 | We benchmark several state-of-the-art segmentation models and provide insight into the difficulties specific to blood detection. | contribution 0.48 | 0.94 | 0.15 | asserted 1.00 |
| s7 | 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. | future work 0.99 | 0.17 | 0.25 | hedged possibility 0.47 |
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Original abstract
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.
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These works stress the importance of hemostasis management, but general segmentation algorithms, such as Meta's Segment Anything Model (SAM) [ref] are unable to adapt to either the visual context of a surgical scene or the geometry of pooled blood, shown in Figure [ref].
Segment Anything · Introduction · contest 0.83
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Given initial comparisons to SAM [ref] , it is clear that SOTA general segmentation algorithms are incapable of capturing pooling geometry to the fidelity necessary for a hemostasis assistant, as shown in Figure [ref].
Segment Anything · Discussion · 0.90
Cites as background 1 paper
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More recently, attention-based mechanisms and transformers have been applied to segmentation tasks - in particular, we point to MANet-based models [ref] and SegFormer-based models [ref] , respectively, for their focus on multi-scale quality on medical images.
MISSFormer: An Effective Medical Image Segmentation Transformer · Related Work · 0.86
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