Pedro Henrique Bandeira Diniz1, Thales Levi Azevedo Valente2, João Otávio Bandeira Diniz3, Aristófanes Corrêa Silva4, Marcelo Gattass5, Nina Ventura6, Bernardo Carvalho Muniz7, Emerson Leandro Gasparetto8. 1. Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil. Electronic address: pedro_hbd@hotmail.com. 2. Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil. Electronic address: selaht7@gmail.com. 3. Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil. Electronic address: joao.obd@gmail.com. 4. Federal University of Maranhão - UFMA Applied Computing Group - NCA Av. dos Portugueses, SN, Bacanga, MA, São Luís, 65085-580, Brazil. Electronic address: aricsilva@gmail.com. 5. Pontifical Catholic University of Rio de Janeiro - PUC - RioR. São Vicente, 225, Gávea, RJ, Rio de Janeiro, 22453-900, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br. 6. Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil. Electronic address: niventuraa@gmail.com. 7. Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil. Electronic address: bernardocmuniz@yahoo.com.br. 8. Paulo Niemeyer State Brain Institute - IECR. Lobo Júnior, 2293, Penha -RJ, 21070-060, Brazil. Electronic address: egasparetto@gmail.com.
Abstract
BACKGROUND AND OBJECTIVE: White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. METHODS: The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. RESULTS: The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. CONCLUSIONS: It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions.
BACKGROUND AND OBJECTIVE:White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. METHODS: The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. RESULTS: The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. CONCLUSIONS: It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions.
Authors: João O B Diniz; Darlan B P Quintanilha; Antonino C Santos Neto; Giovanni L F da Silva; Jonnison L Ferreira; Stelmo M B Netto; José D L Araújo; Luana B Da Cruz; Thamila F B Silva; Caio M da S Martins; Marcos M Ferreira; Venicius G Rego; José M C Boaro; Carolina L S Cipriano; Aristófanes C Silva; Anselmo C de Paiva; Geraldo Braz Junior; João D S de Almeida; Rodolfo A Nunes; Roberto Mogami; M Gattass Journal: Multimed Tools Appl Date: 2021-06-24 Impact factor: 2.757