{"id":"https://openalex.org/W4415540091","doi":"https://doi.org/10.1145/3746027.3755696","title":"Symmetrical Awareness Generation for Pelvic Image Segmentation","display_name":"Symmetrical Awareness Generation for Pelvic Image Segmentation","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415540091","doi":"https://doi.org/10.1145/3746027.3755696"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755696","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755696","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5111320246","display_name":"Yize Song","orcid":null},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yize Song","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-8316-2239","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015551771","display_name":"Yunqing Chen","orcid":"https://orcid.org/0000-0002-3504-8472"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunqing Chen","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3504-8472","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhou Wang","orcid":"https://orcid.org/0009-0008-2114-3461"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Wang","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-2114-3461","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420548","display_name":"Cheng Chen","orcid":"https://orcid.org/0000-0002-4203-2145"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Chen","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4203-2145","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052045981","display_name":"Ruoxiu Xiao","orcid":"https://orcid.org/0000-0002-2721-4813"},"institutions":[{"id":"https://openalex.org/I92403157","display_name":"University of Science and Technology Beijing","ror":"https://ror.org/02egmk993","country_code":"CN","type":"education","lineage":["https://openalex.org/I92403157"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoxiu Xiao","raw_affiliation_strings":["School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2721-4813","affiliations":[{"raw_affiliation_string":"School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China","institution_ids":["https://openalex.org/I92403157"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I92403157"],"apc_list":null,"apc_paid":null,"fwci":0.4629,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.66764591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"8806","last_page":"8814"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9717000126838684,"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"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9717000126838684,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9710000157356262,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9571999907493591,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7024000287055969},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5406000018119812},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49939998984336853},{"id":"https://openalex.org/keywords/pelvis","display_name":"Pelvis","score":0.4896000027656555},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.45809999108314514},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.4142000079154968},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.39579999446868896},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.39419999718666077}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7311000227928162},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7024000287055969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6937999725341797},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5633999705314636},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5406000018119812},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49939998984336853},{"id":"https://openalex.org/C2778357063","wikidata":"https://www.wikidata.org/wiki/Q713102","display_name":"Pelvis","level":2,"score":0.4896000027656555},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.45809999108314514},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3765999972820282},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.37229999899864197},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755696","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755696","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G239681883","display_name":null,"funder_award_id":"2023M730226","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7966050950","display_name":null,"funder_award_id":"62301026","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W2962785568","https://openalex.org/W2967079887","https://openalex.org/W3014974815","https://openalex.org/W3133428285","https://openalex.org/W3155710154","https://openalex.org/W4294564767","https://openalex.org/W4312933868","https://openalex.org/W4372336638","https://openalex.org/W4383218413","https://openalex.org/W4391469564","https://openalex.org/W4399803525","https://openalex.org/W4399876352","https://openalex.org/W4403275125","https://openalex.org/W4403780742","https://openalex.org/W4411948860","https://openalex.org/W4414073185"],"related_works":[],"abstract_inverted_index":{"The":[0],"pelvis":[1,27],"is":[2,12,30],"a":[3,53,63,80,102,126,161],"high-incidence":[4],"region":[5],"for":[6,14,26,136,146,213],"trauma,":[7],"and":[8,17,42,74,97,152,170,221],"its":[9],"precise":[10,111],"segmentation":[11,77,153,176],"vital":[13],"clinical":[15],"diagnosis":[16],"treatment.":[18],"Although":[19],"deep":[20],"learning":[21,145,215],"has":[22],"achieved":[23],"high":[24,44],"precision":[25],"segmentation,":[28],"it":[29,142,173],"still":[31],"confronted":[32],"with":[33,79,166,207],"challenges":[34],"such":[35],"as":[36],"the":[37,43,90,115,178,185],"scarcity":[38],"of":[39,46,148,187],"medical":[40,103,205],"data":[41,58],"cost":[45],"annotation.":[47],"Recently,":[48],"generative":[49,66,179],"models":[50],"have":[51],"offered":[52],"viable":[54],"solution":[55],"through":[56],"synthetic":[57],"augmentation.":[59],"Thus,":[60],"we":[61,100,124,140,159],"propose":[62],"novel":[64,127],"text-conditioned":[65],"framework":[67],"that":[68,131],"simultaneously":[69],"produces":[70],"high-fidelity":[71],"CT":[72,163],"images":[73,151,206],"their":[75],"corresponding":[76],"masks,":[78],"special":[81],"focus":[82],"on":[83],"accurately":[84],"generating":[85],"symmetric":[86],"pelvic":[87,121,150,162,180,194],"structures,":[88],"including":[89],"left":[91],"hip":[92,95],"bone,":[93],"right":[94],"bone":[96,122],"sacrum.":[98],"Firstly,":[99],"fine-tuned":[101],"text":[104],"encoder":[105],"to":[106,143,174,192,203],"transform":[107],"detailed":[108],"descriptions":[109],"into":[110],"generation":[112,147],"conditions.":[113],"Considering":[114],"easily":[116,201],"overlooked":[117],"symmetry":[118,208],"attribute":[119],"in":[120,216],"images,":[123],"introduced":[125],"coordinate-aware":[128],"enhancement":[129],"module":[130],"incorporates":[132],"bone-specific":[133],"centroid":[134],"coordinates":[135],"symmetrical":[137],"awareness.":[138],"Finally,":[139],"expanded":[141],"multi-task":[144],"paired":[149],"labels.":[154],"To":[155],"test":[156],"our":[157,188],"method,":[158,189],"released":[160],"image":[164],"dataset":[165,222],"textual":[167],"description":[168],"(CT-PelvisText)":[169],"then":[171],"transferred":[172],"downstream":[175],"using":[177],"image.":[181],"Our":[182,219],"experiments":[183],"demonstrate":[184],"reliability":[186],"which":[190,210],"contributes":[191],"accurate":[193],"segmentation.":[195],"This":[196],"work":[197],"can":[198],"also":[199],"be":[200],"extended":[202],"other":[204],"properties,":[209],"provides":[211],"potential":[212],"efficient":[214],"small-sample":[217],"datasets.":[218],"code":[220],"are":[223],"available":[224],"at:":[225],"https://github.com/CurellaSong/TSA_LDM.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-25T00:00:00"}