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MedCLIP-SAMv2: Towards universal text-driven medical image segmentation.

Taha Koleilat¹, Hojat Asgariandehkordi¹, Hassan Rivaz¹

¹Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada.

Medical Image Analysis

|August 8, 2025

Summary

This summary is machine-generated.

This study introduces MedCLIP-SAMv2, a new framework for medical image segmentation using foundation models like CLIP and Segment-Anything-Model (SAM). It achieves accurate segmentation with less labeled data for diverse medical imaging tasks.

Keywords:

Foundation models Text-driven image segmentation Vision-language models Weakly supervised segmentation

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Area of Science:

Medical Image Analysis
Artificial Intelligence
Computer Vision

Background:

Accurate segmentation of medical images is crucial for diagnosis and treatment.
Current deep learning methods often require extensive labeled data, limiting efficiency and generalizability.
Foundation models offer potential for data-efficient and interactive segmentation.

Purpose of the Study:

To develop a novel framework, MedCLIP-SAMv2, for data-efficient medical image segmentation.
To integrate CLIP and Segment-Anything-Model (SAM) for text-prompted segmentation.
To evaluate the framework in zero-shot and weakly supervised settings across various medical imaging modalities.

Main Methods:

Fine-tuned BiomedCLIP with a Decoupled Hard Negative Noise Contrastive Estimation (DHN-NCE) loss.
Utilized Multi-modal Information Bottleneck (M2IB) for visual prompt generation.
Employed text prompts for segmentation with SAM in zero-shot and weakly supervised settings.

Main Results:

MedCLIP-SAMv2 demonstrated high accuracy in segmenting diverse medical imaging tasks.
The framework showed effectiveness in both zero-shot and weakly supervised segmentation scenarios.
Validation was performed on ultrasound, MRI, X-ray, and CT imaging data.

Conclusions:

MedCLIP-SAMv2 offers a robust and data-efficient solution for medical image segmentation.
The integration of foundation models advances interactive and universal segmentation in clinical settings.
The proposed framework shows promise for improving disease diagnosis and treatment planning.