ArticleJournal of magnetic resonance imaging : JMRI2020
Deep-Learning-Based Neural Tissue Segmentation of MRI in Multiple Sclerosis: Effect of Training Set Size.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture.PLOS digital health · 2026Article
- Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data.Frontiers in artificial intelligence · 2026Article
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition.Diagnostics (Basel, Switzerland) · 2024Article
- How many specimens make a sufficient training set for automated three-dimensional feature extraction?Royal Society open science · 2024Article
- Automatic segmentation of ameloblastoma on ct images using deep learning with limited data.BMC oral health · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Deep learning model fusion improves lung tumor segmentation accuracy across variable training-to-test dataset ratios.Physical and engineering sciences in medicine · 2023Article
- 3D U-Net for automated detection of multiple sclerosis lesions: utility of transfer learning from other pathologies.Frontiers in neuroscience · 2023Article
- Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.NMR in biomedicine · 2022Article
- Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.Journal of digital imaging · 2022Article
- Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.Scientific reports · 2022Article
- Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review).Experimental and therapeutic medicine · 2021Review
- Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.Journal of medical physicsArticle
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6 authors.
Funding
Abstract
backgroundThe dependence of deep-learning (DL)-based segmentation accuracy of brain MRI on the training size is not known. PURPOSE: To determine the required training size for a desired accuracy in brain MRI segmentation in multiple sclerosis (MS) using DL. STUDY TYPE: Retrospective analysis of MRI data acquired as part of a multicenter clinical trial. STUDY POPULATION: In all, 1008 patients with clinically definite MS. FIELD STRENGTH/SEQUENCE: MRIs were acquired at 1.5T and 3T scanners manufactured by GE, Philips, and Siemens with dual turbo spin echo, FLAIR, and T ASSESSMENT: Segmentation results using an automated analysis pipeline and validated by two neuroimaging experts served as the ground truth. A DL model, based on a fully convolutional neural network, was trained separately using 16 different training sizes. The segmentation accuracy as a function of the training size was determined. These data were fitted to the learning curve for estimating the required training size for desired accuracy. STATISTICAL TESTS: The performance of the network was evaluated by calculating the Dice similarity coefficient (DSC), and lesion true-positive and false-positive rates.
resultsThe DSC for lesions showed much stronger dependency on the sample size than gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). When the training size was increased from 10 to 800 the DSC values varied from 0.00 to 0.86 ± 0.016 for T DATA
conclusionExcellent segmentation was achieved with a training size as small as 10 image volumes for GM, WM, and CSF. In contrast, a training size of at least 50 image volumes was necessary for adequate lesion segmentation. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1487-1496.
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