Unifying Segment Anything in Microscopy with Vision-Language Knowledge

TL;DR

This paper introduces uLLSAM, a framework that enhances biomedical image segmentation by integrating vision-language knowledge into the Segment Anything Model, significantly improving performance and generalization across microscopy datasets.

Contribution

The paper presents a novel approach to incorporate multimodal large language models into segmentation models, unifying microscopy segmentation with vision-language understanding.

Findings

11.8% improvement in segmentation accuracy on in-domain datasets

9.2% improvement in out-of-domain dataset performance

State-of-the-art results on multiple microscopy datasets

Abstract

Accurate segmentation of regions of interest in biomedical images holds substantial value in image analysis. Although several foundation models for biomedical segmentation have currently achieved excellent performance on certain datasets, they typically demonstrate sub-optimal performance on unseen domain data. We owe the deficiency to lack of vision-language knowledge before segmentation. Multimodal Large Language Models (MLLMs) bring outstanding understanding and reasoning capabilities to multimodal tasks, which inspires us to leverage MLLMs to inject Vision-Language Knowledge (VLK), thereby enabling vision models to demonstrate superior generalization capabilities on cross-domain datasets. In this paper, we propose a novel framework that seamlessly uses MLLMs to guide SAM in learning microscopy cross-domain data, unifying Segment Anything in Microscopy, named uLLSAM. Specifically, we propose the Vision-Language Semantic Alignment (VLSA) module, which injects VLK into Segment Anything Model (SAM). We find that after SAM receives global VLK prompts, its performance improves significantly, but there are deficiencies in boundary contour perception. Therefore, we further propose Semantic Boundary Regularization (SBR) to regularize SAM. Our method achieves performance improvements of 11.8% in SA across 9 in-domain microscopy datasets, achieving state-of-the-art performance. Our method also demonstrates improvements of 9.2% in SA across 10 out-of-domain datasets, exhibiting strong generalization capabilities. Code is available at https://github.com/ieellee/uLLSAM.