Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study
Open accessAbstract
Objective: To investigate the performance of deep learning (DL) based on fully convolutional neural network (FCNN) in segmenting brain tissues in a large cohort of multiple sclerosis (MS) patients. Methods: We developed a FCNN model to segment brain tissues, including T2-hyperintense MS lesions. The training, validation, and testing of FCNN were based on ~1000 magnetic resonance imaging (MRI) datasets acquired on relapsing–remitting MS patients, as a part of a phase 3 randomized clinical trial. Multimodal MRI data (dual-echo, FLAIR, and T1-weighted images) served as input to the network. Expert validated segmentation was used as the target for training the FCNN. We cross-validated our results using the leave-one-center-out approach. Results: We observed a high average (95% confidence limits) Dice similarity coefficient for all the segmented tissues: 0.95 (0.92–0.98) for white matter, 0.96 (0.93–0.98) for gray matter, 0.99 (0.98–0.99) for cerebrospinal fluid, and 0.82 (0.63–1.0) for T2 lesions. High correlations between the DL segmented tissue volumes and ground truth were observed ( R 2 > 0.92 for all tissues). The cross validation showed consistent results across the centers for all tissues. Conclusion: The results from this large-scale study suggest that deep FCNN can automatically segment MS brain tissues, including lesions, with high accuracy.
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Most MRI and CT image segmentation research has concentrated on brain segmentation, particularly tumors.
“Although MRI and CT image segmentation research includes different organs such as knee cartilage, prostate, and liver, most research work has concentrated on brain segmentation, particularly tumors [295–300].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [298]
The Methods (Data Preprocessing; page unavailable) describe this paper's use of MRI for brain segmentation, so it supports the citation as an example of that research. The paper does not establish that most MRI and CT segmentation research concerns the brain or that tumors are a particular focus; its own study concerns multiple-sclerosis lesions and brain tissues. (LLM, conf=0.85)
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Sorted by citations
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
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- Authors
- Gabr, R. E., Coronado, I., Robinson, M., Sujit, S. J., Datta, S., Sun, X., Allen, W. J., Lublin, F. D., Wolinsky, J. S., & Narayana, P. A.
- Affiliations
- The University of Texas Health Science Center at Houston (US)
- The University of Texas at Tyler (US)
- Texas Advanced Computing Center (US)
- Mount Sinai Medical Center, New York, NY, USA
- Corresponding author
- Gabr, Refaat E
- ORCID
- Venue
- Multiple Sclerosis Journal
- Publisher
- SAGE Publications
- Published
- 2019-06-13
- Volume
- 26
- Issue
- 10
- Pages
- 1217-1226
- ISSN
- 1352-4585 (ISSN-L)
- 1477-0970
- PMC
- PMC6908772
- OpenAlex
- W2951901024
- Cited by
- 92
- References
- 32
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 16 · 2023: 15 · 2022: 14 · 2021: 15 · 2020: 12 · 2019: 2
- FWCI
- 4.81
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Multiple Sclerosis Journal (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (1R56NS105857-01)
- Endowed Chair in Biomedical Engineering
- 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 › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Keywords
- multiple sclerosis, brain tissue segmentation, lesion segmentation, T2 hyperintensity, fully convolutional network, relapsing-remitting multiple sclerosis, multimodal MRI, white matter, gray matter, cerebrospinal fluid, deep learning
- MeSH terms
- Brain, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Multiple Sclerosis, Neural Networks, Computer, White Matter
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