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This work presents the first segmentation study of both diseased and healthyskin in standard camera photographs from a clinical environment. Challengesarise from varied lighting conditions, skin types, backgrounds, andpathological states. For study, 400 clinical photographs (with skinsegmentation masks) representing various pathological states of skin areretrospectively collected from a primary care network. 100 images are used fortraining and fine-tuning, and 300 are used for evaluation. This distributionbetween training and test partitions is chosen to reflect the difficulty inamassing large quantities of labeled data in this domain. A deep learningapproach is used, and 3 public segmentation datasets of healthy skin arecollected to study the potential benefits of pre-training. Two variants ofU-Net are evaluated: U-Net and Dense Residual U-Net. We find that DenseResidual U-Nets have a 7.8% improvement in Jaccard, compared to classical U-Netarchitectures (0.55 vs. 0.51 Jaccard), for direct transfer, where fine-tuningdata is not utilized. However, U-Net outperforms Dense Residual U-Net for bothdirect training (0.83 vs. 0.80) and fine-tuning (0.89 vs. 0.88). The starkperformance improvement with fine-tuning compared to direct transfer and directtraining emphasizes both the need for adequate representative data of diseasedskin, and the utility of other publicly available data sources for this task.

Segmentation of both Diseased and Healthy Skin from Clinical Photographs in a Primary Care Setting