ArticleHuman brain mapping2016
Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis.
Article in Human brain mapping, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 3 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Can Transcranial Electrical Stimulation Facilitate Post-stroke Cognitive Rehabilitation? A Systematic Review and Meta-Analysis.Frontiers in rehabilitation sciences · 2022Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- [Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2022Trial
- [Prior knowledge-guided and border-focused segmentation of ischemic stroke lesions].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Article
- Competition between Tool and Hand Motion Impairs Movement Planning in Limb Apraxia.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Fixel-Based White Matter Correlates of Sentence Comprehension in Post-Stroke Aphasia.Brain sciences · 2025Article
- Article
- Full-head segmentation of MRI with abnormal brain anatomy: model and data release.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Susceptibility to multitasking in stroke is associated to multiple-demand system damage and leads to lateralized visuospatial deficits.Communications biology · 2025Article
- Infratentorial white matter integrity as a potential biomarker for post-stroke aphasia.Brain communications · 2025Article
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Customized Visual Discrimination Digital Therapy According to Visual Field Defects in Chronic Stroke Patients.Journal of clinical neurology (Seoul, Korea) · 2024Article
- Metric comparison of connectome-based lesion-symptom mapping in post-stroke aphasia.Brain communications · 2024Article
- Brainchop: Providing an Edge Ecosystem for Deployment of Neuroimaging Artificial Intelligence Models.Aperture neuro · 2024Article
- Proposed Protocols for Artificial Intelligence Imaging Database in Acute Stroke Imaging.Neurointervention · 2023Article
- Using predictive validity to compare associations between brain damage and behavior.Human brain mapping · 2023Article
- Brain Areas Critical for Picture Naming: A Systematic Review and Meta-Analysis of Lesion-Symptom Mapping Studies.Neurobiology of language (Cambridge, Mass.) · 2023Article
- Indirect structural disconnection-symptom mapping.Brain structure & function · 2022Review
12 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
The gold standard for identifying stroke lesions is manual tracing, a method that is known to be observer dependent and time consuming, thus impractical for big data studies. We propose LINDA (Lesion Identification with Neighborhood Data Analysis), an automated segmentation algorithm capable of learning the relationship between existing manual segmentations and a single T1-weighted MRI. A dataset of 60 left hemispheric chronic stroke patients is used to build the method and test it with k-fold and leave-one-out procedures. With respect to manual tracings, predicted lesion maps showed a mean dice overlap of 0.696 ± 0.16, Hausdorff distance of 17.9 ± 9.8 mm, and average displacement of 2.54 ± 1.38 mm. The manual and predicted lesion volumes correlated at r = 0.961. An additional dataset of 45 patients was utilized to test LINDA with independent data, achieving high accuracy rates and confirming its cross-institutional applicability. To investigate the cost of moving from manual tracings to automated segmentation, we performed comparative lesion-to-symptom mapping (LSM) on five behavioral scores. Predicted and manual lesions produced similar neuro-cognitive maps, albeit with some discussed discrepancies. Of note, region-wise LSM was more robust to the prediction error than voxel-wise LSM. Our results show that, while several limitations exist, our current results compete with or exceed the state-of-the-art, producing consistent predictions, very low failure rates, and transferable knowledge between labs. This work also establishes a new viewpoint on evaluating automated methods not only with segmentation accuracy but also with brain-behavior relationships. LINDA is made available online with trained models from over 100 patients.
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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.