Summary of Asi-seg: Audio-driven Surgical Instrument Segmentation with Surgeon Intention Understanding, by Zhen Chen et al.
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding
by Zhen Chen, Zongming Zhang, Wenwu Guo, Xingjian Luo, Long Bai, Jinlin Wu, Hongliang Ren, Hongbin Liu
First submitted to arxiv on: 28 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an audio-driven surgical instrument segmentation framework, ASI-Seg, to accurately segment required surgical instruments based on surgeons’ audio commands. The framework leverages intention-oriented multimodal fusion to interpret the segmentation intention from audio commands and retrieve relevant instrument details for segmentation. Additionally, a contrastive learning prompt encoder is devised to effectively distinguish between required and irrelevant instruments. Compared to classical state-of-the-art models and medical SAMs, ASI-Seg demonstrates remarkable advantages in both semantic segmentation and intention-oriented segmentation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in surgical operations where surgeons need help identifying the right tools during surgery. Currently, computers can only identify certain types of tools, not specific ones based on what the surgeon wants to use. To fix this, researchers created an algorithm that uses audio commands from the surgeon to figure out which tool they want and then helps them find it quickly. This makes surgery safer and less stressful for doctors. |
Keywords
* Artificial intelligence * Encoder * Prompt * Semantic segmentation
