TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM

• URL: http://arxiv.org/abs/2406.15764v1
• Date: Sat, 22 Jun 2024 07:00:35 GMT
• Title: TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAM
• Authors: Wenxue Li, Xinyu Xiong, Peng Xia, Lie Ju, Zongyuan Ge,
• Abstract summary: We propose a novel framework that customizes SAM for text-prompted Diabetic Retinopathy (DR) lesion segmentation. Our core idea involves exploiting language cues to inject medical prior knowledge into the vision-only segmentation network. Specifically, to unleash the potential of vision-language models in the recognition of medical concepts, we propose an explicit prior encoder.
• Score: 13.960042520448646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large foundation models, such as the Segment Anything Model (SAM), have demonstrated considerable promise across various tasks. Despite their progress, these models still encounter challenges in specialized medical image analysis, especially in recognizing subtle inter-class differences in Diabetic Retinopathy (DR) lesion segmentation. In this paper, we propose a novel framework that customizes SAM for text-prompted DR lesion segmentation, termed TP-DRSeg. Our core idea involves exploiting language cues to inject medical prior knowledge into the vision-only segmentation network, thereby combining the advantages of different foundation models and enhancing the credibility of segmentation. Specifically, to unleash the potential of vision-language models in the recognition of medical concepts, we propose an explicit prior encoder that transfers implicit medical concepts into explicit prior knowledge, providing explainable clues to excavate low-level features associated with lesions. Furthermore, we design a prior-aligned injector to inject explicit priors into the segmentation process, which can facilitate knowledge sharing across multi-modality features and allow our framework to be trained in a parameter-efficient fashion. Experimental results demonstrate the superiority of our framework over other traditional models and foundation model variants.

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