ArticleHuman brain mapping2019
A comparison of automated lesion segmentation approaches for chronic stroke T1-weighted MRI data.
Article in Human brain mapping, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.Translational psychiatry · 2020Pooled it
- Structural connectome changes as biomarkers of stroke recovery: a longitudinal 7T MRI study.BMC neuroscience · 2026Article
- Stroke recovery beyond initial severity: the complementary roles of brain structure and brain function in acute stroke.Brain communications · 2026Article
- Article
- Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025Review
- Precision-Optimised Post-Stroke Prognoses.Annals of clinical and translational neurology · 2025Article
- Post-stroke changes in brain structure and function can both influence acute upper limb function and subsequent recovery.NeuroImage. Clinical · 2025Article
- Segmenting Small Stroke Lesions with Novel Labeling Strategies.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Segmentation of stroke lesions using transformers-augmented MRI analysis.Human brain mapping · 2024Article
- Is 3T MR nerve-bone fusion imaging a viable alternative to MRI-CBCT to identify the relationship between the inferior alveolar nerve and mandibular third molar.Clinical oral investigations · 2024Article
- Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.Scientific reports · 2023Article
- Predicting Motor Outcomes Using Atlas-Based Voxel Features of Post-Stroke Neuroimaging: A Scoping Review.Neurorehabilitation and neural repair · 2023Article
- The elusive metric of lesion load.Brain structure & function · 2023Review
- Indirect structural disconnection-symptom mapping.Brain structure & function · 2022Review
- Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images.Diagnostics (Basel, Switzerland) · 2022Article
- A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms.Scientific data · 2022Article
- The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.Human brain mapping · 2022Review
- Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue.Frontiers in cardiovascular medicine · 2022Article
- Cross-Attention and Deep Supervision UNet for Lesion Segmentation of Chronic Stroke.Frontiers in neuroscience · 2022Article
- Cognitive outcome is related to functional thalamo-cortical connectivity after paediatric stroke.Brain communications · 2022Article
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3 authors.
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Abstract
Accurate stroke lesion segmentation is a critical step in the neuroimaging processing pipeline for assessing the relationship between poststroke brain structure, function, and behavior. Many multimodal segmentation algorithms have been developed for acute stroke neuroimaging, yet few algorithms are effective with only a single T1-weighted (T1w) anatomical MRI. This is a critical gap because multimodal MRI is not commonly available due to time and cost constraints in the stroke rehabilitation setting. Although several attempts to automate the segmentation of chronic lesions on single-channel T1w MRI have been made, these approaches have not been systematically evaluated on a large dataset. We performed an exhaustive review of the literature and identified one semiautomated and three fully automated approaches for segmentation of chronic stroke lesions using T1w MRI within the last 10 years: Clusterize, automated lesion identification (ALI), Gaussian naïve Bayes lesion detection (lesionGnb), and lesion identification with neighborhood data analysis (LINDA). We evaluated each method on a large T1w stroke dataset (N = 181). LINDA was the most computationally expensive approach, but performed best across the three main evaluation metrics (median values: dice coefficient = 0.50, Hausdorff's distance = 36.34 mm, and average symmetric surface distance = 4.97 mm). lesionGnb had the highest recall/least false negatives (median = 0.80). However, across the automated methods, many lesions were either misclassified (ALI: 28, lesionGnb: 39, LINDA: 45) or not identified (ALI: 24, LINDA: 23, lesionGnb: 0). Segmentation accuracy in all automated methods were influenced by size (small: worst) and stroke territory (brainstem, cerebellum: worst) of the lesion. To facilitate reproducible science, our analysis files have been made publicly available online.
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