# Synergistic Bleeding Region and Point Detection in Laparoscopic Surgical Videos

arXiv: https://arxiv.org/abs/2503.22174 · HTML: https://mlsanity.com/paper/2503.22174/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 59 references that are in the map.
- First posted on arXiv 28 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [Video Polyp Segmentation: A Deep Learning Perspective](https://mlsanity.com/paper/2203.14291/) (2022)
- [SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation](https://mlsanity.com/paper/2107.03332/) (2021)
- [SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation](https://mlsanity.com/paper/2408.08870/) (2024)
- [SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More](https://mlsanity.com/paper/2408.04579/) (2024)
- [Surgical SAM 2: Real-time Segment Anything in Surgical Video by Efficient Frame Pruning](https://mlsanity.com/paper/2408.07931/) (2024)
- [OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers](https://mlsanity.com/paper/2207.02255/) (2022)
- [An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms](https://mlsanity.com/paper/1906.07549/) (2019)
- [SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners](https://mlsanity.com/paper/2408.16768/) (2024)
- [An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion](https://mlsanity.com/paper/2401.15753/) (2024)
- [Surgical Workflow Recognition and Blocking Effectiveness Detection in Laparoscopic Liver Resections with Pringle Maneuver](https://mlsanity.com/paper/2408.10538/) (2024)
- [CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation](https://mlsanity.com/paper/2307.08098/) (2023)
- [Depth-Driven Geometric Prompt Learning for Laparoscopic Liver Landmark Detection](https://mlsanity.com/paper/2406.17858/) (2024)
- [GTPT: Group-based Token Pruning Transformer for Efficient Human Pose Estimation](https://mlsanity.com/paper/2407.10756/) (2024)
- [Evaluation Study on SAM 2 for Class-agnostic Instance-level Segmentation](https://mlsanity.com/paper/2409.02567/) (2024)
- [Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging](https://mlsanity.com/paper/1805.10737/) (2018)
- [HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management](https://mlsanity.com/paper/2403.16286/) (2024)

## Built on by (most cited first, 3 of 3)

- [UniSurgSAM: A Unified Promptable Model for Reliable Surgical Video Segmentation](https://mlsanity.com/paper/2604.03645/) (2026)
- [SurgLQA: Scalable Long-Horizon Surgical Video Question Answering](https://mlsanity.com/paper/2605.17915/) (2026)
- [DBT-Bleed: Dual-Branch Temporal Modeling with Key-Frame Selection for Surgical Bleeding Detection](https://mlsanity.com/paper/2606.22829/) (2026)

## Abstract (arXiv)

Intraoperative bleeding in laparoscopic surgery causes rapid obscuration of the operative field to hinder the surgical process and increases the risk of postoperative complications. Intelligent detection of bleeding areas can quantify the blood loss to assist decision-making, while locating bleeding points helps surgeons quickly identify the source of bleeding and achieve hemostasis in time to improve surgical success rates. To fill the benchmark gap, we first construct a real-world laparoscopic surgical bleeding detection dataset, named SurgBlood, comprising 5,330 frames from 95 surgical video clips with bleeding region and point annotations. Accordingly, we develop a dual-task synergistic online detector called BlooDet, enabling simultaneous detection of bleeding regions and points in laparoscopic surgery. The baseline embraces a dual-branch bidirectional guid- ance design based on Segment Anything Model 2. The mask branch detects bleeding regions through adaptive edge and point prompt embeddings, while the point branch leverages mask memory to induce bleeding point memory modeling and captures point motion direction via inter-frame optical flow. By coupled bidirectional guidance, our framework explores spatial-temporal correlations while exploiting memory modeling to infer current bleeding status. Extensive experiments indicate that our method outperforms 13 counterparts in bleeding detection.