Segmenting skin ulcers and measuring the wound area using deep convolutional networks

Autor(es)

Chino, Daniel Y. T. ^[1] ; Scabora, Lucas C. ^[1] ; Cazzolato, Mirela T. ^[1] ; Jorge, Ana E. S. ^[2] ; Traina-, Jr., Caetano ^[1] ; Traina, Agma J. M. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil

^[2] Univ Fed Sao Carlos, Dept Phys Therapy, Sao Carlos - Brazil

• Tipo de documento: Artigo Científico
• Fonte: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE; v. 191, JUL 2020.
• Citações Web of Science: 1

Resumo

Background and objectives: Bedridden patients presenting chronic skin ulcers often need to be examined at home. Healthcare professionals follow the evolution of the patients' condition by regularly taking pictures of the wounds, as different aspects of the wound can indicate the healing stages of the ulcer, including depth, location, and size. The manual measurement of the wounds' size is often inaccurate, time-consuming, and can also cause discomfort to the patient. In this work, we propose the Automatic Skin Ulcer Region Assessment ASURA framework to accurately segment the wound and automatically measure its size. Methods: ASURA uses an encoder/decoder deep neural network to perform the segmentation, which detects the measurement ruler/tape present in the image and estimates its pixel density. Results: Experimental results show that ASURA outperforms the state-of-the-art methods by up to 16% regarding the Dice score, being able to correctly segment the wound with a Dice score higher than 90%. ASURA automatically estimates the pixel density of the images with a relative error of 5%. When using a semi-automatic approach, ASURA was able to estimate the area of the wound in square centimeters with a relative error of 14%. Conclusions: The results show that ASURA is well-suited for the problem of segmenting and automatically measuring skin ulcers. (C) 2020 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
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/17330-1 - Armazenamento e Operações de Navegação em Grafos em SGBDs Relacionais
• Beneficiário: Lucas de Carvalho Scabora
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 14/25125-3 - Tratando o problema da recuperação por conteúdo de objetos complexos em ambientes com hierarquia de dados e multimodalidade
• Beneficiário: Daniel Yoshinobu Takada Chino
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/24414-2 - Ambiente para integração de técnicas para a extração de características e bases de dados complexos para o projeto MIVisBD
• Beneficiário: Mirela Teixeira Cazzolato
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico