# Autonomous Robotic Suction to Clear the Surgical Field for Hemostasis using Image-based Blood Flow Detection

arXiv: https://arxiv.org/abs/2010.08441 · HTML: https://mlsanity.com/paper/2010.08441/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [Tracking and Mapping in Medical Computer Vision: A Review](https://mlsanity.com/paper/2310.11475/) (2023)
- [General-purpose foundation models for increased autonomy in robot-assisted surgery](https://mlsanity.com/paper/2401.00678/) (2024)
- [Image Based Reconstruction of Liquids from 2D Surface Detections](https://mlsanity.com/paper/2111.11491/) (2021)
- [Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives](https://mlsanity.com/paper/2403.04971/) (2024)
- [HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management](https://mlsanity.com/paper/2403.16286/) (2024)
- [Realistic Data Generation for 6D Pose Estimation of Surgical Instruments](https://mlsanity.com/paper/2406.07328/) (2024)
- [Real-Time Constrained 6D Object-Pose Tracking of An In-Hand Suture Needle for Minimally Invasive Robotic Surgery](https://mlsanity.com/paper/2210.11973/) (2022)
- [BleedOrigin: Dynamic Bleeding Source Localization in Endoscopic Submucosal Dissection via Dual-Stage Detection and Tracking](https://mlsanity.com/paper/2507.15094/) (2025)
- [Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning](https://mlsanity.com/paper/2411.14622/) (2024)
- [SurgIRL: Towards Life-Long Learning for Surgical Automation by Incremental Reinforcement Learning](https://mlsanity.com/paper/2409.15651/) (2024)
- [DBT-Bleed: Dual-Branch Temporal Modeling with Key-Frame Selection for Surgical Bleeding Detection](https://mlsanity.com/paper/2606.22829/) (2026)
- [SurgVIL: Scaling Surgical Robot Imitation Learning with Open-source Surgical Videos](https://mlsanity.com/paper/2608.16058/) (2026)

## Abstract (arXiv)

Autonomous robotic surgery has seen significant progression over the last decade with the aims of reducing surgeon fatigue, improving procedural consistency, and perhaps one day take over surgery itself. However, automation has not been applied to the critical surgical task of controlling tissue and blood vessel bleeding--known as hemostasis. The task of hemostasis covers a spectrum of bleeding sources and a range of blood velocity, trajectory, and volume. In an extreme case, an un-controlled blood vessel fills the surgical field with flowing blood. In this work, we present the first, automated solution for hemostasis through development of a novel probabilistic blood flow detection algorithm and a trajectory generation technique that guides autonomous suction tools towards pooling blood. The blood flow detection algorithm is tested in both simulated scenes and in a real-life trauma scenario involving a hemorrhage that occurred during thyroidectomy. The complete solution is tested in a physical lab setting with the da Vinci Research Kit (dVRK) and a simulated surgical cavity for blood to flow through. The results show that our automated solution has accurate detection, a fast reaction time, and effective removal of the flowing blood. Therefore, the proposed methods are powerful tools to clearing the surgical field which can be followed by either a surgeon or future robotic automation developments to close the vessel rupture.