ArticleAJNR. American journal of neuroradiology2019
Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
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- Automated Ventricle Assessment via Three-dimensional Anatomical Reconstruction (AVA-TAR): a computational toolkit for autonomous lateral ventricle assessment in preclinical hydrocephalus models.bioRxiv : the preprint server for biology · 2026Article
- Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.Scientific reports · 2025Article
- Comparing Lesion Volume Dynamics Between Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder During Remission Using Machine-Learning Segmentation.Journal of clinical neurology (Seoul, Korea) · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Training and assessing convolutional neural network performance in automatic vascular segmentation using Ga-68 DOTATATE PET/CT.The international journal of cardiovascular imaging · 2024Article
- BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.Human brain mapping · 2023Article
- Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning phase error correction for cerebrovascular 4D flow MRI.Scientific reports · 2023Article
- 3D Capsule Networks for Brain Image Segmentation.AJNR. American journal of neuroradiology · 2023Article
- Neuroimaging Patterns of Intracranial Infections: Meningitis, Cerebritis, and Their Complications.Neuroimaging clinics of North America · 2023Review
- Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
- Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.AJNR. American journal of neuroradiology · 2023Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks.Radiology. Artificial intelligence · 2022Article
- Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- A deep learning algorithm for white matter hyperintensity lesion detection and segmentation.Neuroradiology · 2022Article
- Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging.Quantitative imaging in medicine and surgery · 2022Article
- An Integrated Analysis of Clinical, Genomic, and Imaging Features Reveals Predictors of Neurocognitive Outcomes in a Longitudinal Cohort of Pediatric Cancer Survivors, Enriched with CNS Tumors (Rad ART Pro).Frontiers in oncology · 2022Article
- Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.Radiology. Artificial intelligence · 2022Article
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Authors and funding
7 authors.
Funding
Abstract
background and purposeMost brain lesions are characterized by hyperintense signal on FLAIR. We sought to develop an automated deep learning-based method for segmentation of abnormalities on FLAIR and volumetric quantification on clinical brain MRIs across many pathologic entities and scanning parameters. We evaluated the performance of the algorithm compared with manual segmentation and existing automated methods. MATERIALS AND
methodsWe adapted a U-Net convolutional neural network architecture for brain MRIs using 3D volumes. This network was retrospectively trained on 295 brain MRIs to perform automated FLAIR lesion segmentation. Performance was evaluated on 92 validation cases using Dice scores and voxelwise sensitivity and specificity, compared with radiologists' manual segmentations. The algorithm was also evaluated on measuring total lesion volume.
resultsOur model demonstrated accurate FLAIR lesion segmentation performance (median Dice score, 0.79) on the validation dataset across a large range of lesion characteristics. Across 19 neurologic diseases, performance was significantly higher than existing methods (Dice, 0.56 and 0.41) and approached human performance (Dice, 0.81). There was a strong correlation between the predictions of lesion volume of the algorithm compared with true lesion volume (ρ = 0.99). Lesion segmentations were accurate across a large range of image-acquisition parameters on >30 different MR imaging scanners.
conclusionsA 3D convolutional neural network adapted from a U-Net architecture can achieve high automated FLAIR segmentation performance on clinical brain MR imaging across a variety of underlying pathologies and image acquisition parameters. The method provides accurate volumetric lesion data that can be incorporated into assessments of disease burden or into radiologic reports.
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