Unifying Segment Anything in Microscopy with Multimodal Large Language
  Model
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 using MLLMs to guide SAM in
learning microscopy crose-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 prompt
SAM. Our method achieves performance improvements of 7.71% in Dice and 12.10%
in SA across 9 in-domain microscopy datasets, achieving state-of-the-art
performance. Our method also demonstrates improvements of 6.79% in Dice and
10.08% in SA across 10 out-ofdomain datasets, exhibiting strong generalization
capabilities. Code is available at https://github.com/ieellee/uLLSAM.
682bb1d99476a97d3b983da0
Manyu Li
claimed_verified
2025-05-20T07:22:02.593Z
66e01d290b0271487147fdc9
/avatars/2dfb01fba8b781a15555f52f8b92d06c.svg
Manyu Li
user
ieellee
682bb1d99476a97d3b983da1
Ruian He
682bb1d99476a97d3b983da2
Zixian Zhang
682bb1d99476a97d3b983da3
Weimin Tan
682bb1d99476a97d3b983da4
Bo Yan
Unifying Segment Anything in Microscopy with Multimodal Large Language
  Model
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 using MLLMs to guide SAM in
learning microscopy crose-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 prompt
SAM. Our method achieves performance improvements of 7.71% in Dice and 12.10%
in SA across 9 in-domain microscopy datasets, achieving state-of-the-art
performance. Our method also demonstrates improvements of 6.79% in Dice and
10.08% in SA across 10 out-ofdomain datasets, exhibiting strong generalization
capabilities. Code is available at https://github.com/ieellee/uLLSAM.
Injecting Vision-Language Knowledge using MLLMs enhances SAM for better performance and generalization across in-domain and out-of-domain microscopy datasets.
Multimodal Large Language Models MLLMs Vision-Language Knowledge VLK Segment Anything Model SAM Vision-Language Semantic Alignment VLSA Semantic Boundary Regularization SBR Dice SA