{"doi":"10.3389/fneur.2025.1640514","title":"Unsupervised semi-automated MRI segmentation detects cortical lesion expansion in chronic traumatic brain injury","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 ( p < 0.0001). Lesion volume significantly expanded in 37 of 61 measured lesions (60.7%), as defined by a longitudinal volume increase that exceeded inter-rater variability. Longitudinal analyses showed similar changes in lesion volume using the ground-truth lesion segmentations. Inter-scan duration was not associated with the magnitude of lesion growth. While the proposed tool requires further refinement and validation, we show that reliable and efficient semi-automated lesion segmentation is feasible in studies of chronic TBI, creating opportunities to elucidate mechanisms of post-traumatic neurodegeneration.","journal":"Frontiers in Neurology","year":2025,"id":535055,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.96,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1336565,"name":"Alexander S. Atalay","orcid":"0009-0009-1511-7782","position":1,"is_corresponding":false},{"id":364408,"name":"Jian Li","orcid":"0000-0002-1691-8727","position":2,"is_corresponding":false},{"id":1080144,"name":"Evie Sobczak","orcid":null,"position":3,"is_corresponding":false},{"id":363749,"name":"Natalie Gilmore","orcid":"0000-0002-2935-8386","position":4,"is_corresponding":false},{"id":305016,"name":"Samuel B. Snider","orcid":"0000-0002-6965-7890","position":5,"is_corresponding":false},{"id":311802,"name":"Brian C. Healy","orcid":"0000-0001-5272-2425","position":6,"is_corresponding":false},{"id":878390,"name":"Holly Carrington","orcid":"0009-0000-1510-7888","position":7,"is_corresponding":false},{"id":1041649,"name":"Enna Selmanovic","orcid":"0000-0002-5350-4566","position":8,"is_corresponding":false},{"id":1042304,"name":"Ariel Pruyser","orcid":null,"position":9,"is_corresponding":false},{"id":1336894,"name":"Lisa Bura","orcid":null,"position":10,"is_corresponding":false},{"id":796358,"name":"David P. Sheppard","orcid":"0000-0001-8969-9783","position":11,"is_corresponding":false},{"id":710484,"name":"David Hunt","orcid":"0000-0003-0590-874X","position":12,"is_corresponding":false},{"id":628026,"name":"Alan C. Seifert","orcid":"0000-0001-7877-4813","position":13,"is_corresponding":false},{"id":278375,"name":"Yelena G. Bodien","orcid":"0000-0003-4858-2903","position":14,"is_corresponding":false},{"id":27685,"name":"Jeanne M. Hoffman","orcid":"0000-0002-3525-3839","position":15,"is_corresponding":false},{"id":340918,"name":"Christine L. Mac Donald","orcid":null,"position":16,"is_corresponding":false},{"id":27686,"name":"Kristen Dams-O'Connor","orcid":"0000-0002-2506-0216","position":17,"is_corresponding":false},{"id":109379,"name":"Brian L. Edlow","orcid":"0000-0001-7235-8456","position":18,"is_corresponding":false},{"id":818379,"name":"Holly J. Freeman","orcid":"0009-0004-8217-2937","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-19T02:51:52.019261Z","pmid":"41170325","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}