{"id":"https://openalex.org/W4385849099","doi":"https://doi.org/10.48550/arxiv.2308.07003","title":"Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks","display_name":"Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks","publication_year":2023,"publication_date":"2023-08-14","ids":{"openalex":"https://openalex.org/W4385849099","doi":"https://doi.org/10.48550/arxiv.2308.07003"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2308.07003","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.07003","pdf_url":"https://arxiv.org/pdf/2308.07003","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2308.07003","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007230088","display_name":"L. Fisch","orcid":"https://orcid.org/0000-0001-6424-7676"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fisch, Lukas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092645209","display_name":"Stefan Zumdick","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zumdick, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077817893","display_name":"Carlotta Barkhau","orcid":"https://orcid.org/0000-0002-7546-9183"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barkhau, Carlotta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051242537","display_name":"Daniel Emden","orcid":"https://orcid.org/0000-0001-7459-6634"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emden, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047848421","display_name":"Jan Ernsting","orcid":"https://orcid.org/0000-0003-4649-2111"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ernsting, Jan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035949410","display_name":"Ramona Leenings","orcid":"https://orcid.org/0000-0002-9137-7510"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leenings, Ramona","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037629380","display_name":"Kelvin Sarink","orcid":"https://orcid.org/0000-0002-4840-5619"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sarink, Kelvin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038716495","display_name":"Nils R. Winter","orcid":"https://orcid.org/0000-0002-6241-1492"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Winter, Nils R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003115770","display_name":"Benjamin Risse","orcid":"https://orcid.org/0000-0001-5691-4029"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Risse, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067261122","display_name":"Udo Dannlowski","orcid":"https://orcid.org/0000-0002-0623-3759"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dannlowski, Udo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5055279410","display_name":"Tim Hahn","orcid":"https://orcid.org/0000-0001-6541-3795"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hahn, Tim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9983999729156494,"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":0.996399998664856,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9926999807357788,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7766914367675781},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7748308777809143},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7683809995651245},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7181973457336426},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6945584416389465},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.644412636756897},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.6252032518386841},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5457493662834167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5332569479942322},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5324626564979553},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.5092164278030396},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5071837902069092},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49078139662742615},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4289359450340271},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4082203209400177},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09258630871772766},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06023845076560974}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7766914367675781},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7748308777809143},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7683809995651245},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7181973457336426},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6945584416389465},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.644412636756897},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.6252032518386841},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5457493662834167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5332569479942322},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5324626564979553},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.5092164278030396},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5071837902069092},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49078139662742615},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4289359450340271},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4082203209400177},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09258630871772766},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06023845076560974},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2308.07003","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.07003","pdf_url":"https://arxiv.org/pdf/2308.07003","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},{"id":"doi:10.48550/arxiv.2308.07003","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2308.07003","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2308.07003","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.07003","pdf_url":"https://arxiv.org/pdf/2308.07003","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.49000000953674316}],"awards":[{"id":"https://openalex.org/G1116979012","display_name":null,"funder_award_id":"Dan3/012/17","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G4266443790","display_name":null,"funder_award_id":"HA7070/2-2","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6335375501","display_name":null,"funder_award_id":"HA7070/3","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G8819810260","display_name":null,"funder_award_id":"HA7070/4","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385849099.pdf","grobid_xml":"https://content.openalex.org/works/W4385849099.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W3104750253","https://openalex.org/W4389060404","https://openalex.org/W2973136608","https://openalex.org/W3012828488","https://openalex.org/W4286233748","https://openalex.org/W4254054209","https://openalex.org/W4200334192","https://openalex.org/W4289129312","https://openalex.org/W4391935352","https://openalex.org/W2952835238"],"abstract_inverted_index":{"Brain":[0],"extraction":[1,47,87,175],"in":[2,13,26,34,73,98,150,191],"magnetic":[3],"resonance":[4],"imaging":[5],"(MRI)":[6],"data":[7],"is":[8,20],"an":[9],"important":[10],"segmentation":[11,19,40,107],"step":[12],"many":[14],"neuroimaging":[15],"preprocessing":[16],"pipelines.":[17],"Image":[18],"one":[21,189],"of":[22,122,130,164,179,188],"the":[23,31,131,186],"research":[24],"fields":[25],"which":[27],"deep":[28,54,78],"learning":[29,79],"had":[30],"biggest":[32],"impact":[33],"recent":[35],"years":[36],"enabling":[37,185],"high":[38],"precision":[39],"with":[41,75,116],"minimal":[42],"compute.":[43],"Consequently,":[44],"traditional":[45],"brain":[46,86,174],"methods":[48,80,143],"are":[49,144],"now":[50],"being":[51],"replaced":[52],"by":[53,176],"learning-based":[55],"methods.":[56],"Here,":[57],"we":[58],"used":[59],"a":[60,83,94,99,110,117,161,177],"unique":[61],"dataset":[62],"comprising":[63],"568":[64],"T1-weighted":[65],"(T1w)":[66],"MR":[67],"images":[68],"from":[69],"191":[70],"different":[71],"studies":[72],"combination":[74],"cutting":[76],"edge":[77],"to":[81,147,159,182],"build":[82],"fast,":[84],"high-precision":[85],"tool":[88],"called":[89],"deepbet.":[90],"deepbet":[91,157],"uses":[92],"LinkNet,":[93],"modern":[95],"UNet":[96],"architecture,":[97],"two":[100],"stage":[101],"prediction":[102],"process.":[103],"This":[104],"increases":[105],"its":[106],"performance,":[108],"setting":[109],"novel":[111],"state-of-the-art":[112],"performance":[113],"during":[114],"cross-validation":[115],"median":[118],"Dice":[119,151,162],"score":[120,163],"(DSC)":[121],"99.0%":[123],"on":[124,194],"unseen":[125],"datasets,":[126],"outperforming":[127],"current":[128,142,183],"state":[129],"art":[132],"models":[133],"(DSC":[134],"=":[135,139],"97.8%":[136],"and":[137],"DSC":[138],"97.9%).":[140],"While":[141],"more":[145],"sensitive":[146],"outliers,":[148],"resulting":[149],"scores":[152],"as":[153,155],"low":[154,195],"76.5%,":[156],"manages":[158],"achieve":[160],">":[165],"96.9%":[166],"for":[167],"all":[168],"samples.":[169],"Finally,":[170],"our":[171],"model":[172],"accelerates":[173],"factor":[178],"~10":[180],"compared":[181],"methods,":[184],"processing":[187],"image":[190],"~2":[192],"seconds":[193],"level":[196],"hardware.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}