LIMIS: Towards Language-based Interactive Medical Image Segmentation
This addresses the need for hands-free interactive segmentation in medical scenarios, though it is incremental as it adapts existing models to a new domain.
The authors tackled the problem of interactive medical image segmentation by introducing LIMIS, the first purely language-based model, which allows radiologists to use language to adapt segmentation masks, achieving high-quality results as confirmed by experts on three public datasets.
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.