让医生仅用语言就能精准分割医学影像，无需手操作。
LIMIS: Towards Language-based Interactive Medical Image Segmentation
- 用语言指令控制医学图像分割，无需点击或画图。
- 在三个公开数据集上实现高质量分割，专家认可其准确性和易用性。
- 适合手术中、重症监护等双手受限的临床场景使用。
本文提出LIMIS：首个完全基于语言的交互式医学图像分割模型。通过将Grounded SAM适配至医学领域，并设计语言驱动的交互策略，使放射科医生能仅通过语言输入融入专业知识来调整分割结果。LIMIS利用医学基础模型生成高质量初始分割掩码，用户可仅用语言对掩码进行修正，适用于医生需腾出双手的临床场景。我们在三个公开医学数据集上评估了LIMIS的性能与可用性，领域专家确认其分割质量高且交互体验良好。
原文摘要 · Abstract (English)
Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain and designing a language-based model interaction strategy that allows radiologists to incorporate their knowledge into the segmentation process. LIMIS produces high-quality initial segmentation masks by leveraging medical foundation models and allows users to adapt segmentation masks using only language, opening up interactive segmentation to scenarios where physicians require using their hands for other tasks. We evaluate LIMIS on three publicly available medical datasets in terms of performance and usability with experts from the medical domain confirming its high-quality segmentation masks and its interactive usability.
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