Application of StyleGAN Architecture for Generating Venous Leg Ulcer Images

Area of Science:

• Medical imaging
• Artificial intelligence in healthcare
• Wound healing research

Background:

• Venous leg ulcers (VLUs) are common chronic wounds significantly impacting patient quality of life.
• Accurate wound data is crucial for monitoring healing and training predictive machine learning models.
• Current data collection methods face challenges in generating sufficient high-quality datasets.

Purpose of the Study:

• To propose a novel method for synthesizing realistic venous leg ulcer images.
• To enhance the process of gathering quality wound data for machine learning applications.
• To address the data scarcity issue in VLU research.

Main Methods:

• Utilized a generative adversarial network (GAN) based on the StyleGAN architecture.
• Synthesized new VLU images from an existing manually collected dataset.
• Validated the quality and realism of synthesized images through clinical assessment.

Main Results:

• Successfully generated novel venous leg ulcer images using StyleGAN.
• Synthesized samples were deemed valid by experienced clinicians.
• Demonstrated the potential of GANs for augmenting VLU datasets.

Conclusions:

• Generative adversarial networks offer a promising approach to increase the volume of VLU data.
• Synthesized data can support the development of machine learning models for VLU healing prediction.
• This method facilitates improved clinical care and research in chronic wound management.