Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization

• URL: http://arxiv.org/abs/2504.17628v1
• Date: Thu, 24 Apr 2025 14:50:10 GMT
• Title: Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization
• Authors: Abderrachid Hamrani, Daniela Leizaola, Renato Sousa, Jose P. Ponce, Stanley Mathis, David G. Armstrong, Anuradha Godavarty,
• Abstract summary: This study introduces the Attention Diffusion Zero-shot Unsupervised System (ADZUS)<n>It is a novel text-guided diffusion model that performs wound segmentation without relying on labeled training data.<n>Trials demonstrate ADZUS surpasses traditional and state-of-the-art segmentation models, achieving an IoU of 86.68% and the highest precision of 94.69%.
• Score: 0.09423257767158631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diabetic foot ulcers (DFUs) pose a significant challenge in healthcare, requiring precise and efficient wound assessment to enhance patient outcomes. This study introduces the Attention Diffusion Zero-shot Unsupervised System (ADZUS), a novel text-guided diffusion model that performs wound segmentation without relying on labeled training data. Unlike conventional deep learning models, which require extensive annotation, ADZUS leverages zero-shot learning to dynamically adapt segmentation based on descriptive prompts, offering enhanced flexibility and adaptability in clinical applications. Experimental evaluations demonstrate that ADZUS surpasses traditional and state-of-the-art segmentation models, achieving an IoU of 86.68\% and the highest precision of 94.69\% on the chronic wound dataset, outperforming supervised approaches such as FUSegNet. Further validation on a custom-curated DFU dataset reinforces its robustness, with ADZUS achieving a median DSC of 75\%, significantly surpassing FUSegNet's 45\%. The model's text-guided segmentation capability enables real-time customization of segmentation outputs, allowing targeted analysis of wound characteristics based on clinical descriptions. Despite its competitive performance, the computational cost of diffusion-based inference and the need for potential fine-tuning remain areas for future improvement. ADZUS represents a transformative step in wound segmentation, providing a scalable, efficient, and adaptable AI-driven solution for medical imaging.

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