{"id":"https://openalex.org/W7163223647","doi":"https://doi.org/10.48550/arxiv.2606.01293","title":"ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI","display_name":"ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI","publication_year":2026,"publication_date":"2026-05-31","ids":{"openalex":"https://openalex.org/W7163223647","doi":"https://doi.org/10.48550/arxiv.2606.01293"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.01293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.01293","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137675853","display_name":"Ashiqur Rahman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rahman, Ashiqur","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137679519","display_name":"Muhammad E. H. Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chowdhury, Muhammad E. H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120944977","display_name":"Md. Abu Sayed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sayed, Md. Abu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018817790","display_name":"Md Sharjis Ibne Wadud","orcid":"https://orcid.org/0000-0001-5710-4933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wadud, Md. Sharjis Ibne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137658813","display_name":"Abu Naser Md. Arafat","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arafat, Abu Naser Md.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111171289","display_name":"Mehedi Hasan Prince","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Prince, Mehedi Hasan","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":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/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.9656999707221985,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.9656999707221985,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T11184","display_name":"Neonatal and fetal brain pathology","score":0.010099999606609344,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.007499999832361937,"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":0.692300021648407},{"id":"https://openalex.org/keywords/upsampling","display_name":"Upsampling","score":0.619700014591217},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5436999797821045},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.35760000348091125},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.35569998621940613},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.34779998660087585},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3472000062465668},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.336899995803833},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.3215000033378601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7372000217437744},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7160999774932861},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.692300021648407},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.619700014591217},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5436999797821045},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4410000145435333},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.35760000348091125},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.29910001158714294},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.27790001034736633},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.2630000114440918},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.2563999891281128},{"id":"https://openalex.org/C157787499","wikidata":"https://www.wikidata.org/wiki/Q13479657","display_name":"Real-time MRI","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.01293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.01293","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.01293","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"segmentation":[1,108],"of":[2,16,29,129,169,178,185,189],"fetal":[3,30],"brain":[4],"tissues":[5],"in":[6,43],"Magnetic":[7],"Resonance":[8],"Imaging":[9],"(MRI)":[10],"is":[11,119],"critical":[12],"for":[13,91,136,203],"early":[14],"diagnosis":[15],"congenital":[17],"abnormalities":[18],"and":[19,35,61,106,114,132,142,163,187,196],"improving":[20],"prenatal":[21],"care.":[22],"However,":[23],"the":[24,99,134,145,152],"task":[25],"remains":[26],"difficult":[27],"because":[28],"motion,":[31],"low":[32],"tissue":[33],"contrast,":[34],"major":[36],"anatomical":[37,104],"variability":[38],"throughout":[39],"gestational":[40],"ages,":[41],"particularly":[42],"segmenting":[44],"complex":[45],"structures":[46],"such":[47,158],"as":[48,159],"white":[49],"matter,":[50,52,57],"gray":[51,56],"lateral":[53],"ventricles,":[54],"deep":[55,74],"extra-cerebrospinal":[58],"fluid,":[59],"cerebellum,":[60],"brainstem.":[62],"As":[63],"a":[64,72,79,83,172],"solution":[65],"to":[66,102],"these":[67],"difficulties,":[68],"this":[69],"research":[70],"introduces":[71],"novel":[73],"learning":[75],"model":[76,154],"that":[77],"combines":[78],"ResNet-34":[80],"encoder":[81],"with":[82,171],"lightweight":[84],"decoder":[85,135],"leveraging":[86],"multi-layer":[87],"perceptron":[88],"(MLP)":[89],"modules":[90],"adaptive":[92],"feature":[93],"refinement.":[94],"This":[95],"design":[96],"specifically":[97],"enhances":[98],"model's":[100],"ability":[101],"preserve":[103],"boundaries":[105],"mitigate":[107],"errors":[109],"caused":[110],"by":[111,121],"motion":[112],"artifacts":[113],"intensity":[115],"inhomogeneities.":[116],"Computational":[117],"efficiency":[118],"achieved":[120],"reducing":[122],"parameter":[123],"count,":[124],"employing":[125],"bilinear":[126],"upsampling":[127],"instead":[128],"transposed":[130],"convolutions,":[131],"optimizing":[133],"speed":[137],"without":[138],"sacrificing":[139],"accuracy.":[140],"Trained":[141],"validated":[143],"on":[144],"FeTA":[146],"2021":[147],"dataset":[148],"using":[149],"5-fold":[150],"cross-validation,":[151],"proposed":[153],"outperforms":[155],"baseline":[156],"architectures":[157],"UNet,":[160],"UNet++,":[161],"DeepLabV3,":[162],"DeepLabV3+,":[164],"achieving":[165],"an":[166],"average":[167],"Accuracy":[168],"97.37%":[170],"mean":[173,180],"Dice":[174],"Similarity":[175],"Coefficient":[176],"(DSC)":[177],"90.33%,":[179],"Intersection":[181],"over":[182],"Union":[183],"(IoU)":[184],"86.93%,":[186],"Precision":[188],"90.83%.":[190],"Additionally,":[191],"its":[192],"fast":[193],"inference":[194],"time":[195],"reduced":[197],"computational":[198],"load":[199],"make":[200],"it":[201],"well-suited":[202],"integration":[204],"into":[205],"real-time":[206],"clinical":[207],"workflows.":[208]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
