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