{"id":"https://openalex.org/W7148596567","doi":"https://doi.org/10.48550/arxiv.2604.00469","title":"Automated Detection of Multiple Sclerosis Lesions on 7-tesla MRI Using U-net and Transformer-based Segmentation","display_name":"Automated Detection of Multiple Sclerosis Lesions on 7-tesla MRI Using U-net and Transformer-based Segmentation","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148596567","doi":"https://doi.org/10.48550/arxiv.2604.00469"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00469","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00469","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00469","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072858561","display_name":"Michael Maynord","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Maynord, Michael","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132759363","display_name":"Minghui Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Minghui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083553427","display_name":"Cornelia Ferm\u00fcller","orcid":"https://orcid.org/0000-0003-2044-2386"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferm\u00fcller, Cornelia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132801203","display_name":"Seongjin Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Seongjin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132761635","display_name":"Yuxin Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Yuxin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050158285","display_name":"Shishir Dahal","orcid":"https://orcid.org/0000-0002-7987-3239"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dahal, Shishir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132826800","display_name":"Daniel M. Harrison","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Harrison, Daniel M.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5072858561"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10137","display_name":"Multiple Sclerosis Research Studies","score":0.8561000227928162,"subfield":{"id":"https://openalex.org/subfields/2734","display_name":"Pathology and Forensic Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10137","display_name":"Multiple Sclerosis Research Studies","score":0.8561000227928162,"subfield":{"id":"https://openalex.org/subfields/2734","display_name":"Pathology and Forensic Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.02669999934732914,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.020600000396370888,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7283999919891357},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.5929999947547913},{"id":"https://openalex.org/keywords/fluid-attenuated-inversion-recovery","display_name":"Fluid-attenuated inversion recovery","score":0.5813999772071838},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5105000138282776},{"id":"https://openalex.org/keywords/multiple-sclerosis","display_name":"Multiple sclerosis","score":0.4674000144004822},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4205999970436096},{"id":"https://openalex.org/keywords/hyperintensity","display_name":"Hyperintensity","score":0.37770000100135803},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.35249999165534973}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7283999919891357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6611999869346619},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5849000215530396},{"id":"https://openalex.org/C101070640","wikidata":"https://www.wikidata.org/wiki/Q3737215","display_name":"Fluid-attenuated inversion recovery","level":3,"score":0.5813999772071838},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5105000138282776},{"id":"https://openalex.org/C2780640218","wikidata":"https://www.wikidata.org/wiki/Q8277","display_name":"Multiple sclerosis","level":2,"score":0.4674000144004822},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4399000108242035},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C146638467","wikidata":"https://www.wikidata.org/wiki/Q10529587","display_name":"Hyperintensity","level":3,"score":0.37770000100135803},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35249999165534973},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C3019060180","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automated method","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.32420000433921814},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2906000018119812},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2745000123977661},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2781192897","wikidata":"https://www.wikidata.org/wiki/Q822050","display_name":"White matter","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.25699999928474426},{"id":"https://openalex.org/C2778559731","wikidata":"https://www.wikidata.org/wiki/Q23808793","display_name":"Radiomics","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00469","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00469","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00469","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00469","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ultra-high":[0],"field":[1,219],"7-tesla":[2],"(7T)":[3],"MRI":[4],"improves":[5],"visualization":[6],"of":[7,139,164,169,193],"multiple":[8,89],"sclerosis":[9],"(MS)":[10],"white":[11],"matter":[12],"lesions":[13,129],"(WML)":[14],"but":[15],"differs":[16],"sufficiently":[17],"in":[18,217],"contrast":[19],"and":[20,42,64,81,93,95,101,143,166],"artifacts":[21],"from":[22,47,104],"1.5-3T":[23],"imaging":[24],"-":[25],"suggesting":[26],"that":[27,130],"widely":[28],"used":[29],"automated":[30,214],"segmentation":[31],"tools":[32],"may":[33],"not":[34],"translate":[35],"directly.":[36],"We":[37,76],"analyzed":[38],"7T":[39,86,195],"FLAIR":[40,87],"scans":[41],"generated":[43],"reference":[44],"WML":[45],"masks":[46],"Lesion":[48],"Segmentation":[49],"Tool":[50],"(LST)":[51],"outputs":[52],"followed":[53],"by":[54,133],"expert":[55],"manual":[56],"revision.":[57],"As":[58],"external":[59],"comparators,":[60],"we":[61,205],"applied":[62],"LST-LPA":[63,175],"the":[65,105,110,137,173,191],"more":[66],"recent":[67],"LST-AI":[68,124],"ensemble,":[69],"both":[70],"originally":[71],"developed":[72],"on":[73,85,172,187],"lower-field":[74],"data.":[75],"then":[77],"trained":[78,186],"3D":[79],"UNETR":[80],"SegFormer":[82],"transformer-based":[83],"models":[84,185],"at":[88,114,136],"resolutions":[90],"(0.5x0.5x0.5^3,":[91],"1.0x1.0x1.0^3,":[92],"1.5x1.5x2.0^3)":[94],"evaluated":[96],"all":[97],"methods":[98],"using":[99],"voxel-wise":[100,162],"lesion-wise":[102,167,179],"metrics":[103],"BraTS":[106],"2023":[107],"framework.":[108],"On":[109,148],"held-out":[111,150],"test":[112,153],"set":[113],"native":[115,194],"0.5x0.5x0.5^3":[116],"resolution,":[117],"7T-trained":[118,203],"transformers":[119],"achieved":[120,160],"competitive":[121],"overlap":[122],"with":[123],"while":[125],"recovering":[126],"additional":[127],"small":[128],"were":[131],"missed":[132],"classical":[134,174],"methods,":[135],"cost":[138],"some":[140],"boundary":[141],"variability":[142],"occasional":[144],"artifact-related":[145],"false":[146],"positives.":[147],"a":[149,161,209],"7":[151],"T":[152],"set,":[154],"our":[155,202],"best":[156],"transformer":[157],"model":[158],"(SegFormer)":[159],"Dice":[163,168,180],"0.61":[165],"0.20,":[170],"improving":[171],"tool":[176],"(Dice":[177],"0.39,":[178],"0.02).":[181],"Performance":[182],"decreased":[183],"for":[184,197,213],"downsampled":[188],"images,":[189],"underscoring":[190],"value":[192],"resolution":[196],"small-lesion":[198],"detection.":[199],"By":[200],"releasing":[201],"models,":[204],"aim":[206],"to":[207],"provide":[208],"reproducible,":[210],"ready-to-use":[211],"resource":[212],"lesion":[215],"quantification":[216],"ultra-high":[218],"MS":[220],"research":[221],"(https://github.com/maynord/7T-MS-lesion-segmentation).":[222]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}