{"id":"https://openalex.org/W4392248396","doi":"https://doi.org/10.1109/icce59016.2024.10444252","title":"Medical SAM: A Glioma Segmentation Fine-tuning Method for SAM Using Brain MR Images","display_name":"Medical SAM: A Glioma Segmentation Fine-tuning Method for SAM Using Brain MR Images","publication_year":2024,"publication_date":"2024-01-06","ids":{"openalex":"https://openalex.org/W4392248396","doi":"https://doi.org/10.1109/icce59016.2024.10444252"},"language":"en","primary_location":{"id":"doi:10.1109/icce59016.2024.10444252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce59016.2024.10444252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","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/A5100662666","display_name":"Xiaoyu Shi","orcid":"https://orcid.org/0000-0002-0255-6323"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Xiaoyu Shi","raw_affiliation_strings":["Ritsumeikan University,Information Science and Engineering,Kusatsu,Japan","Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Information Science and Engineering,Kusatsu,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010422853","display_name":"Yinhao Li","orcid":"https://orcid.org/0000-0001-6846-9161"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yinhao Li","raw_affiliation_strings":["Ritsumeikan University,Information Science and Engineering,Kusatsu,Japan","Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Information Science and Engineering,Kusatsu,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan","institution_ids":["https://openalex.org/I135768898"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053983094","display_name":"Jingliang Cheng","orcid":"https://orcid.org/0000-0002-6996-329X"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingliang Cheng","raw_affiliation_strings":["The Affiliated Hospital of Zhengzhou University Zhengzhou University,Zhengzhou,China","The Affiliated Hospital of Zhengzhou University Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"The Affiliated Hospital of Zhengzhou University Zhengzhou University,Zhengzhou,China","institution_ids":["https://openalex.org/I38877650"]},{"raw_affiliation_string":"The Affiliated Hospital of Zhengzhou University Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101680272","display_name":"Jie Bai","orcid":"https://orcid.org/0000-0002-7744-1117"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Bai","raw_affiliation_strings":["The Affiliated Hospital of Zhengzhou University Zhengzhou University,Zhengzhou,China","The Affiliated Hospital of Zhengzhou University Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"The Affiliated Hospital of Zhengzhou University Zhengzhou University,Zhengzhou,China","institution_ids":["https://openalex.org/I38877650"]},{"raw_affiliation_string":"The Affiliated Hospital of Zhengzhou University Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057590058","display_name":"Guohua Zhao","orcid":"https://orcid.org/0000-0002-8813-8527"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guohua Zhao","raw_affiliation_strings":["The Affiliated Hospital of Zhengzhou University Zhengzhou University,Zhengzhou,China","The Affiliated Hospital of Zhengzhou University Zhengzhou University, Zhengzhou, China"],"affiliations":[{"raw_affiliation_string":"The Affiliated Hospital of Zhengzhou University Zhengzhou University,Zhengzhou,China","institution_ids":["https://openalex.org/I38877650"]},{"raw_affiliation_string":"The Affiliated Hospital of Zhengzhou University Zhengzhou University, Zhengzhou, China","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044216245","display_name":"Yen\u2010Wei Chen","orcid":"https://orcid.org/0000-0002-5952-0188"},"institutions":[{"id":"https://openalex.org/I135768898","display_name":"Ritsumeikan University","ror":"https://ror.org/0197nmd03","country_code":"JP","type":"education","lineage":["https://openalex.org/I135768898","https://openalex.org/I4390039241"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yen-Wei Chen","raw_affiliation_strings":["Ritsumeikan University,Information Science and Engineering,Kusatsu,Japan","Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan"],"affiliations":[{"raw_affiliation_string":"Ritsumeikan University,Information Science and Engineering,Kusatsu,Japan","institution_ids":["https://openalex.org/I135768898"]},{"raw_affiliation_string":"Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan","institution_ids":["https://openalex.org/I135768898"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100662666"],"corresponding_institution_ids":["https://openalex.org/I135768898"],"apc_list":null,"apc_paid":null,"fwci":1.9247,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.85913059,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994999766349792,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994999766349792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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":0.9987000226974487,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6298955678939819},{"id":"https://openalex.org/keywords/glioma","display_name":"Glioma","score":0.6159840822219849},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6145702004432678},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5623135566711426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5459157824516296},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4537433087825775},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34216421842575073},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21752852201461792},{"id":"https://openalex.org/keywords/cancer-research","display_name":"Cancer research","score":0.05874386429786682}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6298955678939819},{"id":"https://openalex.org/C2778227246","wikidata":"https://www.wikidata.org/wiki/Q1365309","display_name":"Glioma","level":2,"score":0.6159840822219849},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6145702004432678},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5623135566711426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5459157824516296},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4537433087825775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34216421842575073},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21752852201461792},{"id":"https://openalex.org/C502942594","wikidata":"https://www.wikidata.org/wiki/Q3421914","display_name":"Cancer research","level":1,"score":0.05874386429786682}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icce59016.2024.10444252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icce59016.2024.10444252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Consumer Electronics (ICCE)","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":13,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2028911404","https://openalex.org/W2366536035","https://openalex.org/W2769556951","https://openalex.org/W2907750714","https://openalex.org/W2953129827","https://openalex.org/W3094502228","https://openalex.org/W3095319910","https://openalex.org/W3127751679","https://openalex.org/W3160284783","https://openalex.org/W4212949515","https://openalex.org/W4390874575","https://openalex.org/W6795435739"],"related_works":["https://openalex.org/W3208778134","https://openalex.org/W3005931108","https://openalex.org/W4386951147","https://openalex.org/W2887359201","https://openalex.org/W4308767530","https://openalex.org/W2362999506","https://openalex.org/W4205170363","https://openalex.org/W4223451915","https://openalex.org/W4220833452","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Based":[0],"on":[1,75,118,140],"the":[2,24,47,57,96,104,154,169,181,215],"2016":[3],"World":[4],"Health":[5],"Organization":[6],"(WHO)":[7],"Classification":[8],"scheme":[9],"for":[10,20,66,87,124,158,211],"gliomas,":[11],"accurate":[12,212],"glioma":[13,21,27,100,160,177,216,219],"segmentation":[14,89,161,213],"serves":[15],"as":[16,33,85],"a":[17,53,119,200,206,222],"fundamental":[18],"basis":[19],"diagnosis.":[22],"In":[23,144],"realm":[25],"of":[26,59,90,98,106,121,168,185,203,209,214],"diagnosis,":[28],"brain":[29,99,163],"MRI":[30],"has":[31,51],"emerged":[32],"an":[34,149],"indispensable":[35],"diagnostic":[36],"tool":[37],"due":[38],"to":[39,42,103,135,151],"its":[40],"ability":[41],"provide":[43],"comprehensive":[44],"information.":[45],"Over":[46],"past":[48],"decade,":[49],"there":[50],"been":[52],"notable":[54],"surge":[55],"in":[56,189,218,226],"utilization":[58],"machine":[60],"learning":[61,72,132],"techniques,":[62],"particularly":[63],"deep":[64,71,131],"learning,":[65],"processing":[67],"medical":[68,91,125],"images.":[69,165],"These":[70],"methods,":[73,198],"based":[74],"convolutional":[76],"neural":[77],"networks":[78],"or":[79],"transformers,":[80],"proposed":[81,148,170,193],"analogous":[82],"architectures":[83],"such":[84],"U-Net":[86],"precise":[88],"images,":[92],"thereby":[93],"significantly":[94],"enhancing":[95],"accuracy":[97],"segmentation.":[101],"Thanks":[102],"development":[105],"foundation":[107,155],"models,":[108],"models":[109],"pre-trained":[110],"with":[111,127],"large-scale":[112],"datasets":[113],"have":[114],"achieved":[115],"better":[116,137],"results":[117,138],"variety":[120],"tasks.":[122],"However,":[123],"images":[126],"small":[128],"dataset":[129],"sizes,":[130],"methods":[133],"struggle":[134],"achieve":[136],"than":[139],"real-world":[141],"image":[142],"datasets.":[143],"this":[145],"study,":[146],"we":[147],"adapter":[150],"effectively":[152],"fine-tune":[153],"model":[156],"(SAM)":[157],"improved":[159],"using":[162],"MR":[164],"The":[166,192],"effectiveness":[167],"method":[171,194],"is":[172],"validated":[173],"via":[174],"our":[175],"private":[176],"data":[178],"set":[179],"from":[180],"First":[182],"Affiliated":[183],"Hospital":[184],"Zhengzhou":[186],"University":[187],"(FHZU)":[188],"Zhengzhou,":[190],"China.":[191],"outperforms":[195],"current":[196],"state-of-the-art":[197],"achieving":[199],"Dice":[201,227],"coefficient":[202],"87.33%":[204],"and":[205],"Hausdorff":[207],"distance":[208],"10.87":[210],"region":[217],"treatment,":[220],"representing":[221],"significant":[223],"4%":[224],"improvement":[225],"coefficient.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
