Improving U-Net segmentation of cutaneous chronic graft-versus-host disease in clinical photographs with semi-supervised training

Measuring skin involvement in chronic graft-versus-host disease (cGVHD) currently requires expert manual assessment, which is costly, time-consuming, and shows high interrater disagreement (>20% surface area). In our previous work, automated image analysis showed promise for measuring affected skin area under controlled photography conditions. Our aim is to improve the performance of these methods in standard clinical photographs without the need for costly expert annotations using a semi-supervised approach. A baseline U-Net model was trained in a fully supervised manner using 360 3D photographs from 36 cGVHD patients, with expert-marked ground truth contours of affected skin. The model was then iteratively retrained by incorporating an additional 5648 unlabeled photographs from 83 new patients using a semi-supervised method. Testing on clinical photographs of 20 held-out patients, the median surface area error improved from 19.2% (interquartile range 6.3 - 33.8) at baseline to 10.2% (4.5 - 22.6) after retraining. Semi-supervised training therefore provides an effective method for translating a pre-trained U-Net segmentation model to standard clinical photographs, without the need for additional expert annotations. Such models could help standardize cGVHD assessment and tracking, alleviating the need for costly expert evaluations and providing a reliable tool that would significantly enhance the current standard of manual assessment.