Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network
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.
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Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error.
“Deep learning techniques have the potential to create new screening tools, predict diseases, improve diagnostic accuracy and accelerate clinical tasks, whilst also reducing costs and human error [9–17].”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [16]
Deep learning is increasingly used in medical imaging for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.
“In medical imaging, deep learning is increasingly used for tasks such as automated lesion detection, segmentation and registration to assist clinicians in disease diagnosis and surgical planning.”
From Deep Learning Strategies for Ultrasound in Pregnancy (2020) · cites this paper as [16]
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Diniz, J. O. B., Diniz, P. H. B., Valente, T. L. A., Silva, A. C., & Paiva, A. C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.01.005 · Cited by 34 · see citing works
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Self-citation
- Authors
- Diniz, P. H. B., Valente, T. L. A., Diniz, J. O. B., Silva, A. C., Gattass, M., Ventura, N., Muniz, B. C., & Gasparetto, E. L.
- Affiliations
- Pontifícia Universidade Católica do Rio de Janeiro (BR)
- Universidade Federal do Maranhão (BR)
- Instituto Estadual do Cérebro Paulo Niemeyer (BR)
- Corresponding author
- Diniz, Pedro Henrique Bandeira
- ORCID
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 167
- Pages
- 49-63
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2799452775
- Cited by
- 34
- References
- 120
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 9 · 2020: 5 · 2019: 2 · 2018: 1
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- white matter lesions, brain MRI, FLAIR imaging, convolutional neural networks, lesion detection, image segmentation, automated lesion detection
- MeSH terms
- Deep Learning, Algorithms, Brain, Diagnosis, Computer-Assisted, False Positive Reactions, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Prevalence, Databases, Factual, Neural Networks, Computer, White Matter
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