GAN-Based ROI Image Translation Method for Predicting Post-Hair Transplant Surgery Images

The study developed a novel GAN-based ROI image translation method to predict post-hair transplant surgery images, focusing on the region of interest (ROI) to enhance image quality and accuracy. Utilizing CycleGAN for image translation and segmentation models like U-net, the method demonstrated superior performance compared to existing models, achieving 23% higher SSIM, 452% higher IoU, and 42% better FID scores. The ensemble approach improved ROI detection, effectively preserving non-surgical areas and maintaining individual characteristics. The method was trained on 1,394 preoperative and 896 postoperative images, proving robust across various image angles and offering precise predictions crucial for patient satisfaction.