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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

A superpixel-driven deep learning approach for the analysis of dermatological wounds

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Autor(es):
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]
Número total de Autores: 7
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 5
Tipo de documento: Artigo Científico
Fonte: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE; v. 183, JAN 2020.
Citações Web of Science: 2
Resumo

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)

Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
Beneficiário:Agma Juci Machado Traina
Linha de fomento: Auxílio à Pesquisa - Temático