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GloW-VSNet: A scribble-based weakly supervised framework for global-view vitiligo lesion segmentation.

Yuheng Wang¹, Yuhan Zheng², Chloe Yue³

¹Department of Dermatology and Skin Science, The University of British Columbia, Vancouver, Canada; School of Biomedical Engineering, The University of British Columbia, Vancouver, Canada; Photomedicine Institute and Centre for Clinical Epidemiology and Evaluation, Vancouver Coast Health Research Institute, Vancouver, Canada; Departments of Population Health Sciences and Basic and Translational Research, BC Cancer, Vancouver, Canada; Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada.

Medical Image Analysis

|December 30, 2025

Summary

This summary is machine-generated.

We developed GloW-VSNet, a new method for segmenting vitiligo lesions in images. This approach uses minimal annotations to accurately identify vitiligo, improving disease monitoring and treatment assessment.

Keywords:

Computer-aided diagnosis Scribble-based annotation Spatial attention Vitiligo segmentation Weakly supervised learning

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Area of Science:

Dermatology
Medical Image Analysis
Computer Vision

Background:

Accurate vitiligo lesion segmentation is crucial for disease management.
Challenges include indistinct borders, complex backgrounds, and artifacts in clinical images.
Fully supervised methods demand extensive, costly data annotation.

Purpose of the Study:

To introduce GloW-VSNet, a novel scribble-guided weakly supervised segmentation method for global-view vitiligo detection.
To overcome limitations of fully supervised approaches in vitiligo lesion segmentation.
To enhance objective quantification of vitiligo for clinical applications.

Main Methods:

Developed GloW-VSNet, a weakly supervised segmentation model using scribble annotations.
Integrated differentiable feature clustering and spatial attention mechanisms.
Implemented spatial continuity optimization for natural lesion distribution and computational efficiency.

Main Results:

GloW-VSNet achieved state-of-the-art performance on multiple public and private vitiligo datasets.
Demonstrated improved segmentation accuracy despite challenging image conditions.
Showcased effectiveness in handling small and sparse vitiligo lesions.

Conclusions:

GloW-VSNet represents a significant advancement in weakly supervised global-view vitiligo segmentation.
The method addresses a critical research gap, enabling more objective disease assessment.
Offers a practical solution for improved vitiligo severity quantification and treatment monitoring.