{"id":"https://openalex.org/W4416962057","doi":"https://doi.org/10.1109/ist66504.2025.11268425","title":"Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation","display_name":"Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4416962057","doi":"https://doi.org/10.1109/ist66504.2025.11268425"},"language":null,"primary_location":{"id":"doi:10.1109/ist66504.2025.11268425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042655412","display_name":"Eirini Cholopoulou","orcid":null},"institutions":[{"id":"https://openalex.org/I145722265","display_name":"University of Thessaly","ror":"https://ror.org/04v4g9h31","country_code":"GR","type":"education","lineage":["https://openalex.org/I145722265"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Eirini Cholopoulou","raw_affiliation_strings":["University of Thessaly,Department of Computer Science and Biomedical,Lamia,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Thessaly,Department of Computer Science and Biomedical,Lamia,Greece","institution_ids":["https://openalex.org/I145722265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032958433","display_name":"Dimitrios E. Diamantis","orcid":"https://orcid.org/0000-0003-4384-8557"},"institutions":[{"id":"https://openalex.org/I145722265","display_name":"University of Thessaly","ror":"https://ror.org/04v4g9h31","country_code":"GR","type":"education","lineage":["https://openalex.org/I145722265"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimitrios E. Diamantis","raw_affiliation_strings":["University of Thessaly,Department of Computer Science and Biomedical,Lamia,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Thessaly,Department of Computer Science and Biomedical,Lamia,Greece","institution_ids":["https://openalex.org/I145722265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020655974","display_name":"Dimitris K. Iakovidis","orcid":"https://orcid.org/0000-0002-5027-5323"},"institutions":[{"id":"https://openalex.org/I145722265","display_name":"University of Thessaly","ror":"https://ror.org/04v4g9h31","country_code":"GR","type":"education","lineage":["https://openalex.org/I145722265"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Dimitris K. Iakovidis","raw_affiliation_strings":["University of Thessaly,Department of Computer Science and Biomedical,Lamia,Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Thessaly,Department of Computer Science and Biomedical,Lamia,Greece","institution_ids":["https://openalex.org/I145722265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145722265"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.30631801,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.5899999737739563,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.5899999737739563,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.1429000049829483,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.05310000106692314,"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":0.8334000110626221},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.6574000120162964},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5522000193595886},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.5238999724388123},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5182999968528748},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48570001125335693},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4690999984741211}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8334000110626221},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7293000221252441},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.6574000120162964},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6306999921798706},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5522000193595886},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5182999968528748},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4887000024318695},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48570001125335693},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4690999984741211},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C157787499","wikidata":"https://www.wikidata.org/wiki/Q13479657","display_name":"Real-time MRI","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.31940001249313354},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2533000111579895}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist66504.2025.11268425","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268425","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1987512289","https://openalex.org/W2194775991","https://openalex.org/W2884436604","https://openalex.org/W2914493539","https://openalex.org/W2928133111","https://openalex.org/W3006169930","https://openalex.org/W3009563704","https://openalex.org/W3015788359","https://openalex.org/W3096947210","https://openalex.org/W3117442186","https://openalex.org/W3172681723","https://openalex.org/W3189766340","https://openalex.org/W3194760139","https://openalex.org/W4283160212","https://openalex.org/W4286303971","https://openalex.org/W4295765955","https://openalex.org/W4321438556","https://openalex.org/W4366526300","https://openalex.org/W4390720784","https://openalex.org/W4390815453","https://openalex.org/W4391932469","https://openalex.org/W4401582602","https://openalex.org/W4401751810","https://openalex.org/W4403005414","https://openalex.org/W4410246486","https://openalex.org/W4411979130"],"related_works":[],"abstract_inverted_index":{"The":[0,112],"segmentation":[1,39,87,168],"of":[2,119,159],"anatomical":[3,61,90,161],"structures":[4,62],"in":[5,10,36,70,116,169,202],"medical":[6,37],"images":[7],"and":[8,18,30,48,68,73,127,144,173],"particularly":[9],"MRI":[11,57,86,157,203],"scans,":[12,172],"is":[13,53,114],"essential":[14],"for":[15,56,85,164,175],"clinical":[16],"diagnosis":[17],"monitoring":[19],"disease":[20],"progression.":[21],"While":[22],"Deep":[23],"Learning":[24],"(DL)":[25],"architectures,":[26],"such":[27],"as":[28],"U-Net":[29],"its":[31,200],"extensions":[32],"are":[33,63],"very":[34],"effective":[35],"image":[38],"tasks,":[40],"they":[41],"often":[42],"struggle":[43],"with":[44,124],"preserving":[45],"fine-grained":[46,142],"details":[47,143],"global":[49,145],"contextual":[50,146],"information.":[51,147],"This":[52],"especially":[54],"challenging":[55],"data":[58],"segmentation,":[59,179],"where":[60],"characterized":[64],"by":[65],"irregular":[66],"boundaries":[67],"variations":[69],"shape,":[71],"contrast,":[72],"scale.":[74],"To":[75],"address":[76],"this":[77],"challenge,":[78],"we":[79],"propose":[80],"a":[81,96],"novel":[82],"DL":[83],"architecture":[84,94],"across":[88,195],"different":[89,160],"structures.":[91],"Specifically,":[92],"the":[93,137,150,181],"introduces":[95],"module,":[97],"named":[98],"Multi-Resolution":[99],"Feature":[100],"Fusion":[101],"(MRFF),":[102],"that":[103,190],"can":[104],"be":[105],"easily":[106],"integrated":[107,115],"into":[108],"any":[109],"U-Net-like":[110],"architecture.":[111],"MRFF":[113],"all":[117],"levels":[118],"an":[120],"encode-decoder":[121],"structure,":[122],"along":[123],"attention":[125],"mechanisms":[126],"skip":[128],"connections":[129],"to":[130,139],"extract":[131],"features":[132],"at":[133],"multiple":[134,196],"resolutions,":[135],"enabling":[136],"model":[138],"capture":[140],"both":[141],"We":[148],"evaluate":[149],"MRFFU-Net":[151,191],"on":[152],"two":[153],"publicly":[154],"available":[155],"benchmark":[156],"datasets":[158],"targets;":[162],"one":[163,174],"Cerebrospinal":[165],"Fluid":[166],"(CSF)":[167],"spinal":[170],"MR":[171],"left":[176],"atrium":[177],"cardiac":[178],"from":[180],"Medical":[182],"Segmentation":[183],"Decathlon":[184],"(MSD)":[185],"challenge.":[186],"Experimental":[187],"results":[188],"indicate":[189],"outperforms":[192],"state-of-the-art":[193],"models":[194],"evaluation":[197],"metrics,":[198],"demonstrating":[199],"effectiveness":[201],"segmentation.":[204]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-12-03T00:00:00"}