在视频中增加高效的实时手术仪器细分,使用点跟踪和细分任何东西
Zijian Wu1, Adam Schmidt1, Peter Kazanzides2
1Robotics and Control Laboratory, Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada.
Healthcare technology letters
|January 16, 2025
概括
本研究引入了一种新的框架,使用轻量级的SegmentAnything模型 (SAM) 和点跟踪来实现高效的手术仪器细分. 该方法实现了最先进的性能和高推断速度,使机器人手术中的实时应用成为可能.
科学领域:
- 计算机视觉 计算机视觉
- 医疗机器人 医疗机器人
- 人工智能的人工智能
背景情况:
- 分段任何模型 (SAM) 提供了强大的细分,但由于高计算成本和每提示,在机器人手术中面临局限性.
- 像增强现实指导这样的临床应用需要最小的用户干预和高效的推断.
研究的目的:
- 为机器人辅助手术开发一个高效和可通用的外科手术仪器细分框架.
- 为了解决手术背景下SAM的计算和提示限制.
主要方法:
- 采用轻量级的SAM变体和微调用于手术场景.
- 与微调的SAM集成了在线点追踪器,以使用稀疏点提示细分.
- 通过在视频中追踪点来确保时间的一致性,即使有遮蔽或偏离视野.
主要成果:
- 与最先进的半监督方法XMem相比,在EndoVis 2015数据集 (84.8 IoU, 91.0 Dice) 上取得了更高的性能.
- 在外体和体内外科手术数据集上证明了与完全监督的方法可比的性能.
- 在没有标签的STIR数据集上展示了强大的零射击概括.
- 在GeForce RTX 4060上达到超过25 FPS的推理速度,在4090 GPU上达到90 FPS.
结论:
- 拟议的框架有效地提高了SAM在手术仪器细分方面的效率和通用性.
- 该方法显示了机器人辅助手术实时应用的巨大潜力,包括增强现实指导.
- 轻量级SAM和点跟踪的结合为推进外科计算机视觉提供了一个有希望的方向.
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