Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network

• Published in: Computer methods and programs in biomedicine 2018-12, Vol.167, p.49-63
• Main Authors: Diniz, Pedro Henrique Bandeira, Valente, Thales Levi Azevedo, Diniz, João Otávio Bandeira, Silva, Aristófanes Corrêa, Gattass, Marcelo, Ventura, Nina, Muniz, Bernardo Carvalho, Gasparetto, Emerson Leandro
• Format: Article
• Language: English
• Subjects: Algorithms; Brain - diagnostic imaging; Computer-aided detection; Convolutional neural network; Databases, Factual; Deep Learning; Diagnosis, Computer-Assisted; False Positive Reactions; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; Medical images; Neural Networks (Computer); Pattern Recognition, Automated; Prevalence; Reproducibility of Results; Sensitivity and Specificity; SLIC0; White Matter - diagnostic imaging; White Matter - injuries; White matter lesion
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2018.04.011

•This work investigates computational method for automatic detection of white matter lesion regions in magnetic resonance images.•The white matter lesion regions was detected in MRI using SLIC zero parameter and convolutional neural networks techniques.•The method is applied in a private database with 91 patients•The method uses image processing and pattern recognition.•The method achieved accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79%, without the use of false positives reduction step, in detection of white matter lesion 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.