{"id":"https://openalex.org/W4412939673","doi":"https://doi.org/10.1109/access.2025.3595458","title":"MFSE-TransUNet: A Thyroid Nodule Ultrasound Image Segmentation Network Integrated With Dynamic Feature Calibration and Edge Enhancement","display_name":"MFSE-TransUNet: A Thyroid Nodule Ultrasound Image Segmentation Network Integrated With Dynamic Feature Calibration and Edge Enhancement","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412939673","doi":"https://doi.org/10.1109/access.2025.3595458"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3595458","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3595458","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3595458","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067027693","display_name":"Ye Lu","orcid":"https://orcid.org/0000-0001-8523-9595"},"institutions":[{"id":"https://openalex.org/I22716506","display_name":"Lanzhou University of Technology","ror":"https://ror.org/03panb555","country_code":"CN","type":"education","lineage":["https://openalex.org/I22716506"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ye Lu","raw_affiliation_strings":["Department of Computer and Communication, Lanzhou University of Technology, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Communication, Lanzhou University of Technology, Lanzhou, China","institution_ids":["https://openalex.org/I22716506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102910179","display_name":"Jianqiang Jing","orcid":"https://orcid.org/0009-0004-9668-1062"},"institutions":[{"id":"https://openalex.org/I22716506","display_name":"Lanzhou University of Technology","ror":"https://ror.org/03panb555","country_code":"CN","type":"education","lineage":["https://openalex.org/I22716506"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaojiao Jing","raw_affiliation_strings":["Department of Computer and Communication, Lanzhou University of Technology, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer and Communication, Lanzhou University of Technology, Lanzhou, China","institution_ids":["https://openalex.org/I22716506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398462","display_name":"Wenbo Zhang","orcid":"https://orcid.org/0000-0002-8601-802X"},"institutions":[{"id":"https://openalex.org/I2799293361","display_name":"Baoji University of Arts and Sciences","ror":"https://ror.org/05nx0xs09","country_code":"CN","type":"education","lineage":["https://openalex.org/I2799293361"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Zhang","raw_affiliation_strings":["School of Computer, Baoji University of Arts and Sciences, Baoji, China","BaoJi University of Arts and Sciences school of computer, Xi&#x2019;an, China"],"affiliations":[{"raw_affiliation_string":"School of Computer, Baoji University of Arts and Sciences, Baoji, China","institution_ids":["https://openalex.org/I2799293361"]},{"raw_affiliation_string":"BaoJi University of Arts and Sciences school of computer, Xi&#x2019;an, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076560874","display_name":"Yali Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I16295237","display_name":"Gansu Agricultural University","ror":"https://ror.org/05ym42410","country_code":"CN","type":"education","lineage":["https://openalex.org/I16295237"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yali Kong","raw_affiliation_strings":["College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China","institution_ids":["https://openalex.org/I16295237"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5067027693"],"corresponding_institution_ids":["https://openalex.org/I22716506"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10943834,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":null,"first_page":"137209","last_page":"137218"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10862","display_name":"AI in cancer detection","score":0.9882000088691711,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9678999781608582,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9441999793052673,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6493786573410034},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6013789176940918},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5967090129852295},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5940228700637817},{"id":"https://openalex.org/keywords/nodule","display_name":"Nodule (geology)","score":0.5671033263206482},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5400136113166809},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5389424562454224},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5377609133720398},{"id":"https://openalex.org/keywords/image-enhancement","display_name":"Image enhancement","score":0.49353182315826416},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.4620470404624939},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.4596507251262665},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42407864332199097},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.34607475996017456},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3417395353317261},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.32284051179885864},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3145428001880646},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2123313844203949},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1360732614994049},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.06980481743812561}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6493786573410034},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6013789176940918},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5967090129852295},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5940228700637817},{"id":"https://openalex.org/C2776731575","wikidata":"https://www.wikidata.org/wiki/Q2916245","display_name":"Nodule (geology)","level":2,"score":0.5671033263206482},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5400136113166809},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5389424562454224},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5377609133720398},{"id":"https://openalex.org/C3017601658","wikidata":"https://www.wikidata.org/wiki/Q545981","display_name":"Image enhancement","level":3,"score":0.49353182315826416},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.4620470404624939},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4596507251262665},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42407864332199097},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.34607475996017456},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3417395353317261},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.32284051179885864},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3145428001880646},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2123313844203949},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1360732614994049},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.06980481743812561},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3595458","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3595458","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:7db655031be0473fbb6741e12310d253","is_oa":true,"landing_page_url":"https://doaj.org/article/7db655031be0473fbb6741e12310d253","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":"IEEE Access, Vol 13, Pp 137209-137218 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3595458","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3595458","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2027685755","https://openalex.org/W2114866336","https://openalex.org/W2194775991","https://openalex.org/W3027739765","https://openalex.org/W3120201671","https://openalex.org/W3127751679","https://openalex.org/W3128646645","https://openalex.org/W3165252795","https://openalex.org/W4200368034","https://openalex.org/W4312084891","https://openalex.org/W4312221863","https://openalex.org/W4313544808","https://openalex.org/W4319744372","https://openalex.org/W4321021178","https://openalex.org/W4321368265","https://openalex.org/W4365504511","https://openalex.org/W4372347372","https://openalex.org/W4384009057","https://openalex.org/W4384159609","https://openalex.org/W4385192112","https://openalex.org/W4385752982","https://openalex.org/W4385984738","https://openalex.org/W4386045807","https://openalex.org/W4391127477","https://openalex.org/W4396620846","https://openalex.org/W4396653350","https://openalex.org/W4396973073","https://openalex.org/W4402667905","https://openalex.org/W4403791281","https://openalex.org/W4405511492","https://openalex.org/W4406610635","https://openalex.org/W4407943043","https://openalex.org/W4408250502","https://openalex.org/W6750469568","https://openalex.org/W6862574146"],"related_works":["https://openalex.org/W1964806738","https://openalex.org/W4243779904","https://openalex.org/W2332066440","https://openalex.org/W4377691549","https://openalex.org/W2056973590","https://openalex.org/W3164196203","https://openalex.org/W2475288000","https://openalex.org/W2116510815","https://openalex.org/W17460865","https://openalex.org/W2372578044"],"abstract_inverted_index":{"Ultrasound":[0],"imaging":[1],"is":[2,36,102],"a":[3,60],"commonly":[4],"used":[5],"auxiliary":[6],"diagnostic":[7],"method":[8],"for":[9,40],"detecting":[10],"thyroid":[11,71],"nodules.":[12],"However,":[13],"its":[14],"low":[15],"resolution,":[16],"high":[17],"noise":[18],"interference,":[19],"numerous":[20],"artifacts,":[21],"and":[22,29,47,56,124,137,139,146],"blurred":[23],"boundaries":[24],"make":[25],"manual":[26],"annotation":[27],"time-consuming":[28],"highly":[30],"subjective.":[31],"Thus,":[32],"accurate":[33],"pixel-level":[34],"segmentation":[35],"of":[37,134,143,162],"great":[38],"value":[39],"quantifying":[41],"nodule":[42,72,83],"morphology,":[43],"tracking":[44],"lesion":[45],"progression,":[46],"planning":[48],"surgery.":[49],"Although":[50],"the":[51,67,75,111,121,128,150,168],"existing":[52,87],"TransUNet":[53,151],"balances":[54],"local":[55],"global":[57],"features":[58],"through":[59],"hybrid":[61],"CNN-Transformer":[62],"architecture,":[63],"it":[64],"still":[65],"faces":[66],"following":[68],"challenges":[69],"in":[70],"segmentation:":[73],"firstly,":[74],"fixed":[76],"convolutional":[77],"kernels":[78],"struggle":[79],"to":[80,82,93,114,149],"adapt":[81],"morphological":[84],"diversity;":[85],"Secondly,":[86],"multi-scale":[88],"feature":[89],"fusion":[90],"methods":[91],"fail":[92],"consider":[94],"hierarchical":[95],"contribution":[96],"differences;":[97],"Furthermore,":[98],"significant":[99],"edge":[100],"information":[101],"easily":[103],"lost":[104],"during":[105],"upsampling.":[106],"Accordingly,":[107],"this":[108],"study":[109],"proposes":[110],"MFSE-TransUNet":[112],"model":[113],"address":[115],"these":[116],"issues.":[117],"Experimental":[118],"results":[119],"on":[120],"TUD,":[122],"TN3K,":[123],"DDTI":[125],"datasets":[126],"demonstrate":[127],"model\u2019s":[129,169],"effectiveness,":[130],"achieving":[131],"MIoU":[132],"improvements":[133],"10.89%,":[135],"6.02%,":[136],"7.83%,":[138],"Dice":[140,160],"coefficient":[141],"increases":[142],"7.79%,":[144],"3.79%,":[145],"5.21%":[147],"compared":[148],"baseline.":[152],"All":[153],"metrics":[154],"show":[155],"consistent":[156],"improvements,":[157],"with":[158],"cross-dataset":[159],"fluctuations":[161],"less":[163],"than":[164],"1.5%,":[165],"strongly":[166],"demonstrating":[167],"generalization":[170],"capability.":[171]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
