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arXiv 2602.09252cs.CVcs.AIcs.MA
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基于基础模型的手术图像分割迭代细化方法

VLM-Guided Iterative Refinement for Surgical Image Segmentation with Foundation Models

School of Automation Science and Engineering, South China University of Technology, Guangzhou, China(自动化科学与工程学院,华南理工大学,广州,中国);Vanderbilt University, Nashville, TN, USA(范德比尔特大学,纳什维尔,田纳西州,美国);The Pennsylvania State University, University Park, PA , USA(宾夕法尼亚州立大学,大学园,宾夕法尼亚州,美国);Peking University, Beijing , China(北京大学,北京,中国);Virginia Commonwealth University, Richmond, VA, USA(弗吉尼亚共同wealth大学,里士满,弗吉尼亚州,美国);University of California San Diego, San Diego, CA, USA(加州大学圣地亚哥分校,圣地亚哥,加利福尼亚州,美国)

Ange Lou, Yamin Li, Qi Chang, Nan Xi, Luyuan Xie, Zichao Li, Tianyu Luan

AI总结:

本文提出IR-SIS,一种基于自然语言描述的手术图像分割迭代细化系统,结合视觉-语言模型和自适应工作流,实现高精度分割与临床交互。

摘要:

手术图像分割对于机器人辅助手术和术中导航至关重要。然而,现有方法受限于预定义类别,无法生成具有自适应细化的单次预测,并缺乏与临床医生交互的机制。我们提出了IR-SIS,一种接受自然语言描述的手术图像分割迭代细化系统。IR-SIS利用微调的SAM3进行初始分割,采用视觉-语言模型检测器械并评估分割质量,并应用一种自适应选择细化策略的代理工作流。系统通过自然语言反馈支持临床医生在环交互。我们还构建了一个多粒度语言标注数据集,来源于EndoVis2017和EndoVis2018基准。实验表明,该系统在域内和域外数据上均表现出最先进的性能,临床医生的交互提供了额外的改进。我们的工作建立了首个基于语言的手术分割框架,具有自适应自我细化能力。

英文摘要:

Surgical image segmentation is essential for robot-assisted surgery and intraoperative guidance. However, existing methods are constrained to predefined categories, produce one-shot predictions without adaptive refinement, and lack mechanisms for clinician interaction. We propose IR-SIS, an iterative refinement system for surgical image segmentation that accepts natural language descriptions. IR-SIS leverages a fine-tuned SAM3 for initial segmentation, employs a Vision-Language Model to detect instruments and assess segmentation quality, and applies an agentic workflow that adaptively selects refinement strategies. The system supports clinician-in-the-loop interaction through natural language feedback. We also construct a multi-granularity language-annotated dataset from EndoVis2017 and EndoVis2018 benchmarks. Experiments demonstrate state-of-the-art performance on both in-domain and out-of-distribution data, with clinician interaction providing additional improvements. Our work establishes the first language-based surgical segmentation framework with adaptive self-refinement capabilities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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