{"id":"https://openalex.org/W7131123695","doi":"https://doi.org/10.1109/iccvw69036.2025.00765","title":"DeepSeg-MS: A 3D Network Based on Hierarchical Multi-Scale Learning for MRI Multiple Sclerosis Lesion Segmentation","display_name":"DeepSeg-MS: A 3D Network Based on Hierarchical Multi-Scale Learning for MRI Multiple Sclerosis Lesion Segmentation","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W7131123695","doi":"https://doi.org/10.1109/iccvw69036.2025.00765"},"language":null,"primary_location":{"id":"doi:10.1109/iccvw69036.2025.00765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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/A5028086260","display_name":"Alessia Rondinella","orcid":"https://orcid.org/0000-0002-6825-8708"},"institutions":[{"id":"https://openalex.org/I39063666","display_name":"University of Catania","ror":"https://ror.org/03a64bh57","country_code":"IT","type":"education","lineage":["https://openalex.org/I39063666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Alessia Rondinella","raw_affiliation_strings":["University of Catania,Dept. of Mathematics and Computer Science,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Catania,Dept. of Mathematics and Computer Science,Italy","institution_ids":["https://openalex.org/I39063666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076442017","display_name":"Francesco Guarnera","orcid":"https://orcid.org/0000-0002-7703-3367"},"institutions":[{"id":"https://openalex.org/I39063666","display_name":"University of Catania","ror":"https://ror.org/03a64bh57","country_code":"IT","type":"education","lineage":["https://openalex.org/I39063666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Guarnera","raw_affiliation_strings":["University of Catania,Dept. of Mathematics and Computer Science,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Catania,Dept. of Mathematics and Computer Science,Italy","institution_ids":["https://openalex.org/I39063666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091834053","display_name":"Francesco Rundo","orcid":"https://orcid.org/0000-0002-9498-093X"},"institutions":[{"id":"https://openalex.org/I39063666","display_name":"University of Catania","ror":"https://ror.org/03a64bh57","country_code":"IT","type":"education","lineage":["https://openalex.org/I39063666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Rundo","raw_affiliation_strings":["University of Catania,Dept. of Mathematics and Computer Science,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Catania,Dept. of Mathematics and Computer Science,Italy","institution_ids":["https://openalex.org/I39063666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126649515","display_name":"Sebastiano Battiato","orcid":null},"institutions":[{"id":"https://openalex.org/I39063666","display_name":"University of Catania","ror":"https://ror.org/03a64bh57","country_code":"IT","type":"education","lineage":["https://openalex.org/I39063666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sebastiano Battiato","raw_affiliation_strings":["University of Catania,Dept. of Mathematics and Computer Science,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Catania,Dept. of Mathematics and Computer Science,Italy","institution_ids":["https://openalex.org/I39063666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60676559,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7417","last_page":"7426"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.2061000019311905,"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"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.2061000019311905,"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":0.10729999840259552,"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/T10137","display_name":"Multiple Sclerosis Research Studies","score":0.0908999964594841,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7851999998092651},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.5903000235557556},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5669000148773193},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5663999915122986},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5184999704360962},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4481000006198883},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.40709999203681946},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.40380001068115234}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7851999998092651},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7342000007629395},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7226999998092651},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.5903000235557556},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5669000148773193},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5663999915122986},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4481000006198883},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.40709999203681946},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39489999413490295},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38960000872612},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.38420000672340393},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C2780640218","wikidata":"https://www.wikidata.org/wiki/Q8277","display_name":"Multiple sclerosis","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw69036.2025.00765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw69036.2025.00765","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)","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":25,"referenced_works":["https://openalex.org/W1587460175","https://openalex.org/W1901129140","https://openalex.org/W1997983592","https://openalex.org/W2005623950","https://openalex.org/W2180878654","https://openalex.org/W2224254632","https://openalex.org/W2412782625","https://openalex.org/W2489242797","https://openalex.org/W2575552683","https://openalex.org/W2589409328","https://openalex.org/W2745892997","https://openalex.org/W2900299818","https://openalex.org/W3034681889","https://openalex.org/W3083138635","https://openalex.org/W3138516171","https://openalex.org/W4212875960","https://openalex.org/W4221163766","https://openalex.org/W4247247070","https://openalex.org/W4312084109","https://openalex.org/W4321232185","https://openalex.org/W4324128823","https://openalex.org/W4377565370","https://openalex.org/W4390992411","https://openalex.org/W4405487565","https://openalex.org/W4410920378"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"3D":[1,36,156,178,187],"segmentation":[2,97,179,190],"of":[3,39,72,128,168,183],"Multiple":[4],"Sclerosis":[5],"(MS)":[6],"lesions":[7,41,154],"from":[8],"multimodal":[9],"Magnetic":[10],"Resonance":[11],"Imaging":[12],"(MRI)":[13],"is":[14],"crucial":[15],"for":[16,63,194],"assessing":[17],"disease":[18,200],"progression":[19],"and":[20,84,95,121,198],"guiding":[21],"clinical":[22,196],"decisions.":[23],"However,":[24],"existing":[25,176],"deep":[26,58,75],"learning-based":[27],"methods":[28],"often":[29],"struggle":[30],"to":[31,91,105,150],"effectively":[32],"capture":[33],"the":[34,103,126,148],"complex":[35],"spatial":[37,112],"dependencies":[38],"MS":[40,65,153,188],"while":[42,114],"ensuring":[43],"robust":[44],"hierarchical":[45,118],"multi-scale":[46,122],"features":[47,119,133],"learning.":[48,124],"To":[49],"address":[50],"these":[51],"challenges,":[52],"we":[53],"introduce":[54],"DeepSeg-MS,":[55],"a":[56,73,172],"novel":[57,74],"neural":[59],"network":[60],"specifically":[61],"designed":[62],"volumetric":[64],"lesion":[66,109,189],"segmentation.":[67],"Our":[68],"approach":[69],"leverages":[70],"performance":[71],"architecture":[76,185],"that":[77],"embeds":[78],"Squeeze-and-Attention":[79],"(SA),":[80],"Deep":[81],"Supervision":[82],"(DS),":[83],"Atrous":[85],"Spatial":[86],"Pyramid":[87],"Pooling":[88],"(ASPP)":[89],"modules":[90],"enhance":[92],"feature":[93],"extraction":[94],"overall":[96],"accuracy.":[98],"The":[99,181],"SA":[100],"blocks":[101],"allow":[102],"model":[104],"focus":[106],"on":[107,163],"relevant":[108],"regions":[110],"across":[111],"dimensions,":[113],"DS":[115],"ensures":[116],"effective":[117,131],"refinement,":[120],"representation":[123],"Additionally,":[125],"integration":[127],"ASPP":[129],"enables":[130],"multiscale":[132],"aggregation":[134],"by":[135],"capturing":[136],"contextual":[137],"information":[138],"at":[139],"different":[140,166],"receptive":[141],"fields":[142],"through":[143],"parallel":[144],"dilated":[145],"convolutions,":[146],"addressing":[147],"challenges":[149],"segment":[151],"heterogeneous":[152],"in":[155,186],"MRI":[157],"scans.":[158],"DeepSeg-MS":[159],"was":[160],"extensively":[161],"evaluated":[162],"two":[164],"totally":[165],"datasets":[167],"this":[169],"field,":[170],"demonstrating":[171],"significant":[173],"improvement":[174],"over":[175],"state-of-the-art":[177],"methods.":[180],"effectiveness":[182],"our":[184],"offers":[191],"potential":[192],"benefits":[193],"both":[195],"applications":[197],"automated":[199],"monitoring.":[201]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-24T00:00:00"}