Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization
Journal:
arXiv
Published Date:
Apr 24, 2025
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.
