MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation
<|reference_start|>MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation: Segmentation of anatomical structures and pathological regions in medical images is essential for modern clinical diagnosis, disease research, and treatment planning. While significant advancements have been made in deep learning-based segmentation techniques, many of these methods still suffer from limitations in data efficiency, generalizability, and interactivity. As a result, developing precise segmentation methods that require fewer labeled datasets remains a critical challenge in medical image analysis. Recently, the introduction of foundation models like CLIP and Segment-Anything-Model (SAM), with robust cross-domain representations, has paved the way for interactive and universal image segmentation. However, further exploration of these models for data-efficient segmentation in medical imaging is still needed and highly relevant. In this paper, we introduce MedCLIP-SAMv2, a novel framework that integrates the CLIP and SAM models to perform segmentation on clinical scans using text prompts, in both zero-shot and weakly supervised settings. Our approach includes fine-tuning the BiomedCLIP model with a new Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss, and leveraging the Multi-modal Information Bottleneck (M2IB) to create visual prompts for generating segmentation masks from SAM in the zero-shot setting. We also investigate using zero-shot segmentation labels within a weakly supervised paradigm to enhance segmentation quality further. Extensive testing across four diverse segmentation tasks and medical imaging modalities (breast tumor ultrasound, brain tumor MRI, lung X-ray, and lung CT) demonstrates the high accuracy of our proposed framework. Our code is available at https://github.com/HealthX-Lab/MedCLIP-SAMv2.<|reference_end|>
@article{koleilat2024medclip-samv2:,
    title={MedCLIP-SAMv2: Towards Universal Text-Driven Medical Image Segmentation},
    author={Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao},
    journal={arXiv preprint arXiv:2409.19483},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19483},
    primaryClass={cs.CV cs.CL}
}