| Full text | |
| Author(s): |
Blanco, Gustavo
[1]
;
Traina, Agma J. M.
[1]
;
Traina Jr, Caetano
;
Azevedo-Marques, Paulo M.
[2]
;
Jorge, Ana E. S.
[3]
;
de Oliveira, Daniel
[4]
;
Bedo, Marcos V. N.
[5]
Total Authors: 7
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, ICMC, Sao Paulo, SP - Brazil
[2] Univ Sao Paulo, HCFMRP, Ribeirao Preto Med Sch, Sao Paulo, SP - Brazil
[3] Univ Fed Sao Carlos, Dept Phys Therapy, DFisio, Sao Carlos, SP - Brazil
[4] Univ Fed Fluminense, IC, Niteroi, RJ - Brazil
[5] Univ Fed Fluminense, Fluminense Northwest Inst, INFES, Niteroi, RJ - Brazil
Total Affiliations: 5
|
| Document type: | Journal article |
| Source: | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE; v. 183, JAN 2020. |
| Web of Science Citations: | 2 |
| Abstract | |
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. (C) 2019 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD) |
| Grantee: | Agma Juci Machado Traina |
| Support Opportunities: | Research Projects - Thematic Grants |