{"id":"https://openalex.org/W4401751557","doi":"https://doi.org/10.1109/isbi56570.2024.10635383","title":"DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation","display_name":"DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation","publication_year":2024,"publication_date":"2024-05-27","ids":{"openalex":"https://openalex.org/W4401751557","doi":"https://doi.org/10.1109/isbi56570.2024.10635383"},"language":"en","primary_location":{"id":"doi:10.1109/isbi56570.2024.10635383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5090545479","display_name":"Athira Jacob","orcid":"https://orcid.org/0000-0002-5948-3655"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]},{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Athira J Jacob","raw_affiliation_strings":["Siemens Healthineers,Digital Technology and Innovation,Princeton,NJ,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Healthineers,Digital Technology and Innovation,Princeton,NJ,USA","institution_ids":["https://openalex.org/I4210137693","https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080475090","display_name":"Puneet Sharma","orcid":"https://orcid.org/0000-0001-5937-5382"},"institutions":[{"id":"https://openalex.org/I4210137693","display_name":"Siemens (United States)","ror":"https://ror.org/04axb7e79","country_code":"US","type":"company","lineage":["https://openalex.org/I1325886976","https://openalex.org/I4210137693"]},{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Puneet Sharma","raw_affiliation_strings":["Siemens Healthineers,Digital Technology and Innovation,Princeton,NJ,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Healthineers,Digital Technology and Innovation,Princeton,NJ,USA","institution_ids":["https://openalex.org/I4210137693","https://openalex.org/I4210151799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006461848","display_name":"Daniel Rueckert","orcid":"https://orcid.org/0000-0002-5683-5889"},"institutions":[{"id":"https://openalex.org/I2802619606","display_name":"TUM Klinikum","ror":"https://ror.org/04jc43x05","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I2802619606","https://openalex.org/I62916508"]},{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Rueckert","raw_affiliation_strings":["Technical University of Munich,AI in Healthcare and Medicine, Klinikum Rechts der Isar,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Munich,AI in Healthcare and Medicine, Klinikum Rechts der Isar,Germany","institution_ids":["https://openalex.org/I2802619606","https://openalex.org/I62916508"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9988999962806702,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/computer-science","display_name":"Computer science","score":0.6656175255775452},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5716227293014526},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5667591094970703},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4273574948310852},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3514319062232971}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6656175255775452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5716227293014526},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5667591094970703},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4273574948310852},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3514319062232971}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi56570.2024.10635383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi56570.2024.10635383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W156779288","https://openalex.org/W1901129140","https://openalex.org/W1909740415","https://openalex.org/W2101689475","https://openalex.org/W2237767100","https://openalex.org/W2805899143","https://openalex.org/W2963627347","https://openalex.org/W2963926543","https://openalex.org/W2964890799","https://openalex.org/W2970546341","https://openalex.org/W3035507572","https://openalex.org/W3101310341","https://openalex.org/W3130794141","https://openalex.org/W3143455115","https://openalex.org/W4206693420","https://openalex.org/W4391452724","https://openalex.org/W6766621745","https://openalex.org/W6857498734"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Segmentation":[0],"is":[1,61],"often":[2],"the":[3,73,85,90,97,107,125,140,143,182],"first":[4],"step":[5],"in":[6,84,96,113,128,160,165,172],"many":[7],"medical":[8],"image":[9],"analyses":[10],"workflows.":[11],"Deep":[12,33],"learning":[13],"approaches,":[14],"while":[15,119],"giving":[16],"state-of-the-art":[17],"accuracies,":[18],"are":[19],"data":[20,29,82,94,111,127,141,152],"intensive":[21],"and":[22,70,118,134,169],"do":[23],"not":[24],"scale":[25],"well":[26,149],"to":[27,53,72,150,158],"low":[28,151],"regimes.":[30],"We":[31,80,101],"introduce":[32],"Conditional":[34],"Shape":[35],"Models":[36],"2.0,":[37],"which":[38],"uses":[39],"an":[40,45],"edge":[41,51,98],"detector,":[42],"along":[43],"with":[44,142,154],"implicit":[46],"shape":[47,56,59],"function":[48,60],"conditioned":[49],"on":[50,64],"maps,":[52],"leverage":[54],"cross-modality":[55],"information.":[57],"The":[58,146],"trained":[62],"exclusively":[63],"a":[65],"source":[66],"domain":[67,75,87],"(contrasted":[68],"CT)":[69],"applied":[71],"target":[74,86],"of":[76,92,115,124,130,139,156,181],"interest":[77],"(3D":[78],"echocardiography).":[79],"demonstrate":[81],"efficiency":[83],"by":[88],"varying":[89],"amounts":[91],"training":[93,126,183],"used":[95],"detection":[99],"stage.":[100],"observe":[102],"that":[103],"DCSM":[104],"2.0":[105],"outperforms":[106],"baseline":[108],"at":[109,135],"all":[110],"levels":[112],"terms":[114,129],"Hausdorff":[116,173],"distances,":[117],"using":[120,176],"50%":[121],"or":[122,137],"less":[123,138],"average":[131,166],"mesh":[132],"distance,":[133],"10%":[136],"dice":[144,161],"coefficient.":[145],"method":[147],"scales":[148],"regimes,":[153],"gains":[155],"up":[157],"5%":[159],"coefficient,":[162],"2.58":[163],"mm":[164,171],"surface":[167],"distance":[168,174],"21.02":[170],"when":[175],"just":[177],"2%":[178],"(22":[179],"volumes)":[180],"data.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}