SynthesisFrontiers in neuroinformatics2020
Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.
Synthesis in Frontiers in neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.Neuroradiology · 2023Pooled it
- A Robust Dual-Stage Learning-Based Pipeline for Multiclass Segmentation of Multiple Sclerosis Lesions in MRI.Diagnostics (Basel, Switzerland) · 2026Article
- An Explainable Multi-Scale Deep Learning Framework for Multi-Class Brain MRI Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Generalizable spinal cord multiple sclerosis lesion segmentation across MRI contrasts, protocols, and centers.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- Regularization via gradient attribution for multiple sclerosis lesion segmentation.Frontiers in medicine · 2026Article
- A Lesion-Aware Patch Sampling Approach with EfficientNet3D-UNet for Robust Multiple Sclerosis Lesion Segmentation.Journal of imaging · 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
- Fractal-Based Architectures with Skip Connections and Attention Mechanism for Improved Segmentation of MS Lesions in Cervical Spinal Cord.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Federated learning for lesion segmentation in multiple sclerosis: a real-world multi-center feasibility study.Frontiers in neurology · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- LST-AI: a Deep Learning Ensemble for Accurate MS Lesion Segmentation.medRxiv : the preprint server for health sciences · 2024Article
- Early detection of white matter hyperintensities using SHIVA-WMH detector.Human brain mapping · 2024Article
- BIANCA-MS: An optimized tool for automated multiple sclerosis lesion segmentation.Human brain mapping · 2023Article
- AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.Insights into imaging · 2023Article
- Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.NPJ digital medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, there have been multiple works of literature reviewing methods for automatically segmenting multiple sclerosis (MS) lesions. However, there is no literature systematically and individually review deep learning-based MS lesion segmentation methods. Although the previous review also included methods based on deep learning, there are some methods based on deep learning that they did not review. In addition, their review of deep learning methods did not go deep into the specific categories of Convolutional Neural Network (CNN). They only reviewed these methods in a generalized form, such as supervision strategy, input data handling strategy, etc. This paper presents a systematic review of the literature in automated multiple sclerosis lesion segmentation based on deep learning. Algorithms based on deep learning reviewed are classified into two categories through their CNN style, and their strengths and weaknesses will also be given through our investigation and analysis. We give a quantitative comparison of the methods reviewed through two metrics: Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). Finally, the future direction of the application of deep learning in MS lesion segmentation will be discussed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.