  Chronic wounds significantly impact quality of life. If not properly managed,
they can severely deteriorate. Image-based wound analysis could aid in
objectively assessing the wound status by quantifying important features that
are related to healing. However, the high heterogeneity of the wound types,
image background composition, and capturing conditions challenge the robust
segmentation of wound images. We present Detect-and-Segment (DS), a deep
learning approach to produce wound segmentation maps with high generalization
capabilities. In our approach, dedicated deep neural networks detected the
wound position, isolated the wound from the uninformative background, and
computed the wound segmentation map. We evaluated this approach using one data
set with images of diabetic foot ulcers. For further testing, 4 supplemental
independent data sets with larger variety of wound types from different body
locations were used. The Matthews' correlation coefficient (MCC) improved from
0.29 when computing the segmentation on the full image to 0.85 when combining
detection and segmentation in the same approach. When tested on the wound
images drawn from the supplemental data sets, the DS approach increased the
mean MCC from 0.17 to 0.85. Furthermore, the DS approach enabled the training
of segmentation models with up to 90% less training data while maintaining the
segmentation performance.
