Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Sidra Aleem, Fangyijie Wang, Mayug Maniparambil, Eric Arazo, Julia Dietlmeier, Kathleen Curran, Noel E. O' Connor, Suzanne Little; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5184-5193

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM a prompt-driven segmentation model excels in segmentation tasks across diverse domains while CLIP is renowned for its zero-shot recognition capabilities. However their unified potential has not yet been explored in medical image segmentation. To adapt SAM to medical imaging existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically we propose a simple unified framework SaLIP for organ segmentation. Initially SAM is used for part-based segmentation within the image followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM's generated masks. Finally SAM is prompted by the retrieved ROI to segment a specific organ. Thus SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%) lung (50.11%) and fetal head (30.82%) when compared to un-prompted SAM. Code and text prompts are available at: https://github.com/aleemsidra/SaLIP.