ArticleFrontiers in neurology2025
Unsupervised semi-automated MRI segmentation detects cortical lesion expansion in chronic traumatic brain injury.
Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Adaptive multi-stage domain unlearning for white-matter lesion segmentation.Frontiers in medicine · 2026Article
- SynthSR-generated 3D T1-weighted MRI from routine 2D clinical images: Validation for VSRAD analysis.Frontiers in neurology · 2025Article
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Abstract
Traumatic brain injury (TBI) is a risk factor for neurodegeneration and cognitive decline, yet the underlying pathophysiologic mechanisms are incompletely understood. This gap in knowledge is in part related to a lack of reliable and efficient methods for measuring cortical lesions in neuroimaging studies. The objective of this study was to develop a semi-automated lesion detection tool and apply it to an investigation of longitudinal changes in brain structure among individuals with chronic TBI. We identified 24 individuals with chronic moderate-to-severe TBI enrolled in the Late Effects of TBI (LETBI) study who had cortical lesions detected by T1-weighted MRI and underwent two MRI scans at least 2 years apart. Initial MRI scans were performed more than 1 year post-injury, and follow-up scans were performed a median of 3.1 (IQR = 1.7) years later. We leveraged FreeSurfer parcellations of T1-weighted MRI volumes and a recently developed super-resolution technique, SynthSR, to automate the identification of cortical lesions in this longitudinal dataset. Trained raters received the data in a randomized order and manually edited the automated lesion segmentations, yielding a final semi-automated lesion mask for each scan at each time point. Inter-rater variability was assessed in an independent cohort of 10 additional LETBI subjects with cortical lesions. The semi-automated lesion segmentations showed a high level of accuracy compared to "ground truth" lesion segmentations performed via manual segmentation by a separate blinded rater. In a longitudinal analysis of the semi-automated segmentations, lesion volume increased between the two time points with a median volume change of 4.91 (IQR = 12.95) mL (
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