  Background. The image-based identification of distinct tissues within
dermatological wounds enhances patients' care since it requires no intrusive
evaluations. This manuscript presents an approach, we named QTDU, that combines
deep learning models with superpixel-driven segmentation methods for assessing
the quality of tissues from dermatological ulcers.
  Method. QTDU consists of a three-stage pipeline for the obtaining of ulcer
segmentation, tissues' labeling, and wounded area quantification. We set up our
approach by using a real and annotated set of dermatological ulcers for
training several deep learning models to the identification of ulcered
superpixels.
  Results. Empirical evaluations on 179,572 superpixels divided into four
classes showed QTDU accurately spot wounded tissues (AUC = 0.986, sensitivity =
0.97, and specificity = 0.974) and outperformed machine-learning approaches in
up to 8.2% regarding F1-Score through fine-tuning of a ResNet-based model.
Last, but not least, experimental evaluations also showed QTDU correctly
quantified wounded tissue areas within a 0.089 Mean Absolute Error ratio.
  Conclusions. Results indicate QTDU effectiveness for both tissue segmentation
and wounded area quantification tasks. When compared to existing
machine-learning approaches, the combination of superpixels and deep learning
models outperformed the competitors within strong significant levels.
