Segmenting Vitiligo on Clinical Face Images Using CNN Trained on Synthetic and Internet Images

Accurately diagnosing and describing the severity of vitiligo is crucial for prognostication, treatment selection and comparison. Currently, disease severity scores require dermatologists to estimate percentage area of involvement, which is subjected to inter and intra-assessor variability. Previous studies focus on pure skin but vitiligo on the face, which has a more serious impact on patients' quality of life, was completely neglected. Convolutional neural networks (CNNs) have good performance on many segmentation tasks. However, due to data privacy, it is hard to have a large clinical vitiligo face image dataset to train a CNN. To address this challenge, images from two different sources, the Internet and the proposed vitiligo face synthesis algorithm, are employed in training. 843 vitiligo images taken from different viewpoints were collected from the Internet. These images are hugely different from the target clinical images collected according to a newly established international standard. To have more vitiligo face images similar to the target clinical images to enhance segmentation performance, an image synthesis algorithm is proposed. Both synthetic and Internet images are used to train a CNN which is modified from the fully convolutional network (FCN) to segment face vitiligo lesions. The results show that 1) the synthetic images effectively improve segmentation performance; 2) the proposed algorithm achieves 1.06$\%$ error for the face vitiligo area estimation and 3) it is more accurate than two dermatologists and all the previous automated vitiligo segmentation methods, which were designed for segmentation vitiligo on pure skin.