{"id":"https://openalex.org/W4414773303","doi":"https://doi.org/10.3389/fcomp.2025.1677905","title":"Enhancing medical image segmentation via complementary CNN-transformer fusion and boundary perception","display_name":"Enhancing medical image segmentation via complementary CNN-transformer fusion and boundary perception","publication_year":2025,"publication_date":"2025-10-03","ids":{"openalex":"https://openalex.org/W4414773303","doi":"https://doi.org/10.3389/fcomp.2025.1677905"},"language":"en","primary_location":{"id":"doi:10.3389/fcomp.2025.1677905","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fcomp.2025.1677905","pdf_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1677905/pdf","source":{"id":"https://openalex.org/S4210211086","display_name":"Frontiers in Computer Science","issn_l":"2624-9898","issn":["2624-9898"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1677905/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076993228","display_name":"Xiaowei Liu","orcid":"https://orcid.org/0000-0002-0064-6990"},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaowei Liu","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067457756","display_name":"Juanxiu Tian","orcid":"https://orcid.org/0000-0003-4492-219X"},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juanxiu Tian","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023873507","display_name":"Shangrong Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I27688046","display_name":"Hunan Institute of Engineering","ror":"https://ror.org/03zj2rn70","country_code":"CN","type":"education","lineage":["https://openalex.org/I27688046"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangrong Huang","raw_affiliation_strings":["School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Hunan Institute of Engineering, Xiangtan, China","institution_ids":["https://openalex.org/I27688046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101954098","display_name":"Wei Shen","orcid":"https://orcid.org/0009-0003-9347-3842"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]},{"id":"https://openalex.org/I4210156904","display_name":"Third Xiangya Hospital","ror":"https://ror.org/05akvb491","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210156904"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Shen","raw_affiliation_strings":["Coronary Care Unit, Nursing Department, The Third Xiangya Hospital of Central South University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"Coronary Care Unit, Nursing Department, The Third Xiangya Hospital of Central South University, Changsha, China","institution_ids":["https://openalex.org/I4210156904","https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076993228","https://openalex.org/A5101954098"],"corresponding_institution_ids":["https://openalex.org/I139660479","https://openalex.org/I27688046","https://openalex.org/I4210156904"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":1.3734,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85832244,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"7","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":0.9994000196456909,"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":0.9994000196456909,"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":0.9984999895095825,"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":0.9975000023841858,"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":0.676800012588501},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5181999802589417},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.512499988079071},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.5012000203132629},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4896000027656555},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47369998693466187},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.44429999589920044},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4043000042438507}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8052999973297119},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.676800012588501},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6316999793052673},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5354999899864197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5181999802589417},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.512499988079071},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4896000027656555},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47369998693466187},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.44429999589920044},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4043000042438507},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.36390000581741333},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.3361999988555908},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C2780757906","wikidata":"https://www.wikidata.org/wiki/Q5276676","display_name":"Dilation (metric space)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C63099799","wikidata":"https://www.wikidata.org/wiki/Q17147001","display_name":"Image texture","level":4,"score":0.2563999891281128},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3389/fcomp.2025.1677905","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fcomp.2025.1677905","pdf_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1677905/pdf","source":{"id":"https://openalex.org/S4210211086","display_name":"Frontiers in Computer Science","issn_l":"2624-9898","issn":["2624-9898"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:cfdffd13fa674307b2fc77801bf29f95","is_oa":true,"landing_page_url":"https://doaj.org/article/cfdffd13fa674307b2fc77801bf29f95","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Computer Science, Vol 7 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fcomp.2025.1677905","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fcomp.2025.1677905","pdf_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1677905/pdf","source":{"id":"https://openalex.org/S4210211086","display_name":"Frontiers in Computer Science","issn_l":"2624-9898","issn":["2624-9898"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414773303.pdf"},"referenced_works_count":48,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2008359794","https://openalex.org/W2017745767","https://openalex.org/W2021088830","https://openalex.org/W2194775991","https://openalex.org/W2285968993","https://openalex.org/W2560328367","https://openalex.org/W2798122215","https://openalex.org/W2884436604","https://openalex.org/W2997286550","https://openalex.org/W3131500599","https://openalex.org/W3160284783","https://openalex.org/W3168491317","https://openalex.org/W3170841864","https://openalex.org/W3194513480","https://openalex.org/W3199733685","https://openalex.org/W3203841574","https://openalex.org/W3204166336","https://openalex.org/W3204255739","https://openalex.org/W3210645152","https://openalex.org/W4210465915","https://openalex.org/W4214893857","https://openalex.org/W4225536339","https://openalex.org/W4255556797","https://openalex.org/W4281489883","https://openalex.org/W4288068240","https://openalex.org/W4311088269","https://openalex.org/W4312568229","https://openalex.org/W4315705623","https://openalex.org/W4318347829","https://openalex.org/W4319300502","https://openalex.org/W4363650233","https://openalex.org/W4385748643","https://openalex.org/W4386046221","https://openalex.org/W4392383209","https://openalex.org/W4394835773","https://openalex.org/W4400881081","https://openalex.org/W4403424772","https://openalex.org/W4403563630","https://openalex.org/W4405284461","https://openalex.org/W4406314479","https://openalex.org/W4406691739","https://openalex.org/W4407356309","https://openalex.org/W4407398140","https://openalex.org/W4408537528","https://openalex.org/W4409335657","https://openalex.org/W4409426611","https://openalex.org/W4412871044"],"related_works":[],"abstract_inverted_index":{"Introduction":[0],"Vision":[1],"Transformers":[2],"(ViTs)":[3],"show":[4],"promise":[5],"for":[6,22,78,253],"image":[7,13],"recognition":[8],"but":[9],"struggle":[10],"with":[11],"medical":[12,238,259],"segmentation":[14,136,255],"due":[15],"to":[16,28,30,98,110,162,189],"a":[17,47,54,75,87,116,143,173,201,208],"lack":[18],"of":[19,146,176,204,222],"inductive":[20],"biases":[21],"local":[23],"structures":[24],"and":[25,36,44,67,85,95,124,155,227,237,251],"an":[26],"inability":[27],"adapt":[29],"diverse":[31],"modalities":[32],"like":[33],"CT,":[34],"endoscopy,":[35],"dermatology.":[37],"Effectively":[38],"combining":[39],"multi-scale":[40],"features":[41,63,97],"from":[42,64,160,187],"CNNs":[43],"ViTs":[45],"remains":[46],"critical,":[48],"unsolved":[49],"challenge.":[50],"Methods":[51],"We":[52],"propose":[53],"Pyramid":[55],"Feature":[56],"Fusion":[57],"Network":[58],"(PFF-Net)":[59],"that":[60,90],"integrates":[61],"hierarchical":[62],"pre-trained":[65],"CNN":[66],"Transformer":[68],"backbones.":[69],"Its":[70],"dual-branch":[71,228],"architecture":[72],"includes:":[73],"(1)":[74],"region-aware":[76],"branch":[77,89],"global-to-local":[79],"contextual":[80],"understanding":[81],"via":[82],"pyramid":[83,224],"fusion,":[84],"(2)":[86],"boundary-aware":[88],"employs":[91],"orthogonal":[92],"Sobel":[93],"operators":[94],"low-level":[96],"generate":[99],"precise,":[100],"semantic":[101],"boundaries.":[102,127],"These":[103],"boundary":[104],"predictions":[105],"are":[106],"iteratively":[107],"fed":[108],"back":[109],"enhance":[111],"the":[112,149,157,185,212,220,232],"region":[113],"branch,":[114],"creating":[115],"mutually":[117],"reinforcing":[118],"loop":[119],"between":[120,235],"segmenting":[121],"anatomical":[122],"regions":[123],"delineating":[125],"their":[126],"Results":[128],"PFF-Net":[129,141],"achieved":[130,200],"state-of-the-art":[131],"performance":[132],"across":[133,256],"three":[134],"clinical":[135],"tasks.":[137],"On":[138],"polyp":[139],"segmentation,":[140,170,197],"attained":[142],"Dice":[144,174,202],"score":[145,175,203],"91.87%,":[147],"surpassing":[148],"TransUNet":[150],"baseline":[151,214],"(86.96%)":[152],"by":[153,181],"5.6%":[154],"reducing":[156,184],"HD95":[158,186],"metric":[159],"22.25":[161],"11.68":[163],"(a":[164,191],"47.5%":[165],"reduction).":[166,193],"For":[167],"spleen":[168],"CT":[169],"it":[171],"reached":[172],"95.33%,":[177],"outperforming":[178],"ESFPNet-S":[179,213],"(94.92%)":[180],"4.3%":[182],"while":[183],"6.99":[188],"3.35":[190],"52.1%":[192],"In":[194],"skin":[195],"lesion":[196],"our":[198,223],"model":[199],"90.29%,":[205],"which":[206],"represents":[207],"7.3%":[209],"improvement":[210],"over":[211],"(89.64%).":[215],"Discussion":[216],"The":[217,240],"results":[218],"validate":[219],"effectiveness":[221],"fusion":[225],"strategy":[226],"design":[229],"in":[230],"bridging":[231],"domain":[233],"gap":[234],"natural":[236],"images.":[239],"framework":[241],"demonstrates":[242],"strong":[243],"generalization":[244],"on":[245],"small-scale":[246],"datasets,":[247],"proving":[248],"its":[249],"robustness":[250],"potential":[252],"accurate":[254],"highly":[257],"heterogeneous":[258],"imaging":[260],"modalities.":[261]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-10T00:00:00"}
