CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging

• URL: http://arxiv.org/abs/2509.08618v1
• Date: Wed, 10 Sep 2025 14:14:49 GMT
• Title: CLAPS: A CLIP-Unified Auto-Prompt Segmentation for Multi-Modal Retinal Imaging
• Authors: Zhihao Zhao, Yinzheng Zhao, Junjie Yang, Xiangtong Yao, Quanmin Liang, Shahrooz Faghihroohi, Kai Huang, Nassir Navab, M. Ali Nasseri,
• Abstract summary: We propose CLIP-unified Auto-Prompt (CLAPS), a novel method for unified segmentation across diverse tasks and modalities in retinal imaging.<n>Our approach begins by pre-training a CLIP-based image encoder on a large, multi-modal retinal dataset.<n>To unify tasks and resolve ambiguity, we use text prompts enhanced with a unique "modality signature" for each imaging modality.
• Score: 47.04292769940597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in foundation models, such as the Segment Anything Model (SAM), have significantly impacted medical image segmentation, especially in retinal imaging, where precise segmentation is vital for diagnosis. Despite this progress, current methods face critical challenges: 1) modality ambiguity in textual disease descriptions, 2) a continued reliance on manual prompting for SAM-based workflows, and 3) a lack of a unified framework, with most methods being modality- and task-specific. To overcome these hurdles, we propose CLIP-unified Auto-Prompt Segmentation (\CLAPS), a novel method for unified segmentation across diverse tasks and modalities in retinal imaging. Our approach begins by pre-training a CLIP-based image encoder on a large, multi-modal retinal dataset to handle data scarcity and distribution imbalance. We then leverage GroundingDINO to automatically generate spatial bounding box prompts by detecting local lesions. To unify tasks and resolve ambiguity, we use text prompts enhanced with a unique "modality signature" for each imaging modality. Ultimately, these automated textual and spatial prompts guide SAM to execute precise segmentation, creating a fully automated and unified pipeline. Extensive experiments on 12 diverse datasets across 11 critical segmentation categories show that CLAPS achieves performance on par with specialized expert models while surpassing existing benchmarks across most metrics, demonstrating its broad generalizability as a foundation model.

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