{"id":"https://openalex.org/W7138261703","doi":"https://doi.org/10.1609/aaai.v40i8.37606","title":"FGNet: Leveraging Feature-Guided Attention to Refine SAM2 for 3D EM Neuron Segmentation","display_name":"FGNet: Leveraging Feature-Guided Attention to Refine SAM2 for 3D EM Neuron Segmentation","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138261703","doi":"https://doi.org/10.1609/aaai.v40i8.37606"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i8.37606","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i8.37606","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i8.37606","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129688996","display_name":"Zhenghua Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenghua Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129692165","display_name":"Hang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hang Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057493831","display_name":"Zihao Sun","orcid":"https://orcid.org/0000-0001-6337-1712"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zihao Sun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129673799","display_name":"Kai Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129652440","display_name":"Xiaolin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaolin Hu","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":"40","issue":"8","first_page":"6744","last_page":"6752"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.8762999773025513,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.8762999773025513,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12859","display_name":"Cell Image Analysis Techniques","score":0.08990000188350677,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.002899999963119626,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7064999938011169},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6782000064849854},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5684000253677368},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5447999835014343},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.52920001745224},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.48829999566078186},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.44269999861717224},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.43939998745918274},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4302000105381012}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.829800009727478},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7064999938011169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6977999806404114},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6782000064849854},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5447999835014343},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.48829999566078186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45419999957084656},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.43939998745918274},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3492000102996826},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3434000015258789},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i8.37606","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i8.37606","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i8.37606","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i8.37606","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-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":{"Accurate":[0],"segmentation":[1],"of":[2,33,55],"neural":[3],"structures":[4],"in":[5,124,194],"Electron":[6],"Microscopy":[7],"(EM)":[8],"images":[9,57],"is":[10,17,80],"paramount":[11],"for":[12],"neuroscience.":[13],"However,":[14],"this":[15,61],"task":[16],"challenged":[18],"by":[19,47],"intricate":[20],"morphologies,":[21],"low":[22],"signal-to-noise":[23],"ratios,":[24],"and":[25,31,137],"scarce":[26],"annotations,":[27],"limiting":[28],"the":[29,44,86,100,120,155,191],"accuracy":[30],"generalization":[32],"existing":[34,169],"methods.":[35,171],"To":[36,98],"address":[37,190],"these":[38,127],"challenges,":[39],"we":[40,64,103],"seek":[41],"to":[42,58,85,93,115,150],"leverage":[43],"priors":[45],"learned":[46],"visual":[48],"foundation":[49],"models":[50],"on":[51,82,126,162,179],"a":[52,66,105,117,131],"vast":[53],"amount":[54],"natural":[56,83,180],"better":[59],"tackle":[60],"task.":[62],"Specifically,":[63],"propose":[65],"novel":[67],"framework":[68],"that":[69,109,144,175],"can":[70,188],"effectively":[71,189],"transfer":[72],"knowledge":[73],"from":[74,113],"Segment":[75],"Anything":[76],"2":[77],"(SAM2),":[78],"which":[79],"pre-trained":[81,178],"images,":[84,181],"EM":[87,163],"domain.":[88],"We":[89],"first":[90],"use":[91],"SAM2":[92,114,156],"extract":[94],"powerful,":[95],"general-purpose":[96],"features.":[97],"bridge":[99],"domain":[101],"gap,":[102],"introduce":[104],"Feature-Guided":[106],"Attention":[107],"module":[108],"leverages":[110],"semantic":[111],"cues":[112],"guide":[116],"lightweight":[118],"encoder,":[119],"Fine-Grained":[121],"Encoder":[122],"(FGE),":[123],"focusing":[125],"challenging":[128],"regions.":[129],"Finally,":[130],"dual-affinity":[132],"decoder":[133],"generates":[134],"both":[135],"coarse":[136],"refined":[138],"affinity":[139],"maps.":[140],"Experimental":[141],"results":[142],"demonstrate":[143],"our":[145,165],"method":[146,166],"achieves":[147],"performance":[148],"comparable":[149],"state-of-the-art":[151],"(SOTA)":[152],"approaches":[153],"with":[154,184],"weights":[157],"frozen.":[158],"Upon":[159],"further":[160],"fine-tuning":[161],"data,":[164],"significantly":[167],"outperforms":[168],"SOTA":[170],"This":[172],"study":[173],"validates":[174],"transferring":[176],"representations":[177],"when":[182],"combined":[183],"targeted":[185],"domain-adaptive":[186],"guidance,":[187],"specific":[192],"challenges":[193],"neuron":[195],"segmentation.":[196]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}
