{"id":"https://openalex.org/W2960239140","doi":"https://doi.org/10.1109/isbi.2019.8759314","title":"Deep Learning for Volumetric Segmentation in Spatio-Temporal Data: Application to Segmentation of Prostate in DCE-MRI","display_name":"Deep Learning for Volumetric Segmentation in Spatio-Temporal Data: Application to Segmentation of Prostate in DCE-MRI","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2960239140","doi":"https://doi.org/10.1109/isbi.2019.8759314","mag":"2960239140"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2019.8759314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","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/A5049139590","display_name":"Jian Kang","orcid":"https://orcid.org/0000-0003-3797-256X"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jian Kang","raw_affiliation_strings":["School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049771929","display_name":"Gihan Samarasinghe","orcid":"https://orcid.org/0000-0003-4700-9427"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Gihan Samarasinghe","raw_affiliation_strings":["School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031460727","display_name":"Upul Senanayake","orcid":null},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Upul Senanayake","raw_affiliation_strings":["School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056241875","display_name":"Sailesh Conjeti","orcid":"https://orcid.org/0000-0003-3442-0225"},"institutions":[{"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":"Sailesh Conjeti","raw_affiliation_strings":["Computer Aided Medical Procedures, Technische Universit\u00e4t M\u00fcnchen, Munich, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Aided Medical Procedures, Technische Universit\u00e4t M\u00fcnchen, Munich, Germany","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055952724","display_name":"Arcot Sowmya","orcid":"https://orcid.org/0000-0001-9236-5063"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Arcot Sowmya","raw_affiliation_strings":["School of Computer Science and Engineering, University of New South Wales, Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"65"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.991100013256073,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9886000156402588,"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/computer-science","display_name":"Computer science","score":0.8303375244140625},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7781332731246948},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7367845177650452},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7348000407218933},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6662972569465637},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6443920731544495},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5933966636657715},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5643561482429504},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4445459246635437},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.41109010577201843}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8303375244140625},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7781332731246948},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7367845177650452},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7348000407218933},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6662972569465637},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6443920731544495},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5933966636657715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5643561482429504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4445459246635437},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.41109010577201843},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/isbi.2019.8759314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2019.8759314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","raw_type":"proceedings-article"},{"id":"pmh:oai:pub.dzne.de:164493","is_oa":false,"landing_page_url":"https://pub.dzne.de/record/164493","pdf_url":null,"source":{"id":"https://openalex.org/S7407051172","display_name":"DZNE Pub","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE 2019, 61-65 (2019). doi:10.1109/ISBI.2019.8759314","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.4099999964237213}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1966047305","https://openalex.org/W1979693095","https://openalex.org/W1993947467","https://openalex.org/W2012634455","https://openalex.org/W2014914090","https://openalex.org/W2016919721","https://openalex.org/W2049522781","https://openalex.org/W2095705004","https://openalex.org/W2143612262","https://openalex.org/W2145560107","https://openalex.org/W2604790786","https://openalex.org/W2609077090","https://openalex.org/W2755764120","https://openalex.org/W2766076071","https://openalex.org/W2807122651","https://openalex.org/W2949117887","https://openalex.org/W2962914239","https://openalex.org/W2963631529","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6674330103","https://openalex.org/W6736302784"],"related_works":["https://openalex.org/W2762363380","https://openalex.org/W2806236206","https://openalex.org/W2981727625","https://openalex.org/W2901833326","https://openalex.org/W2963637377","https://openalex.org/W2793231006","https://openalex.org/W2737861363","https://openalex.org/W3102015993","https://openalex.org/W3100708836","https://openalex.org/W3123857146","https://openalex.org/W2942988769","https://openalex.org/W2887533690","https://openalex.org/W2787015083","https://openalex.org/W3033139251","https://openalex.org/W3030882797","https://openalex.org/W2974035007","https://openalex.org/W2970975385","https://openalex.org/W2981886702","https://openalex.org/W2902622318","https://openalex.org/W2913844953"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,15,41,49,56,99,120,139,143],"the":[2,13,25,47,57,100],"prostate":[3,58],"in":[4,59],"MR":[5],"images":[6],"is":[7,67],"an":[8],"essential":[9],"step":[10],"that":[11],"underpins":[12],"success":[14],"subsequent":[16],"analysis":[17,40],"methods,":[18],"such":[19],"as":[20],"cancer":[21],"lesion":[22],"detection":[23],"inside":[24],"tumour":[26],"and":[27,51,83,141],"registration":[28],"between":[29],"different":[30],"modalities.":[31],"This":[32],"work":[33],"focuses":[34],"on":[35,115],"leveraging":[36],"deep":[37,106],"learning":[38],"for":[39,46,54,69],"longitudinal":[42],"volumetric":[43,79],"datasets,":[44],"particularly":[45],"task":[48],"segmentation,":[50],"presents":[52],"proof-of-concept":[53],"segmentation":[55,150],"3D+T":[60],"DCE-MRI":[61],"sequences.":[62],"A":[63],"two-stream":[64],"processing":[65],"pipeline":[66],"proposed":[68,110],"this":[70],"task,":[71],"comprising":[72],"a":[73,78,84,116,133,148],"spatial":[74],"stream":[75,86],"modelled":[76],"using":[77,88,105],"fully":[80],"convolutional":[81],"network":[82],"temporal":[85,126],"modeled":[87],"recurrent":[89],"neural":[90,107],"networks":[91],"with":[92,124],"Long-Short-term":[93],"Memory":[94],"(LSTM)":[95],"units.":[96],"The":[97,109],"predictions":[98],"two":[101],"streams":[102],"are":[103],"fused":[104],"networks.":[108],"method":[111],"has":[112],"been":[113],"validated":[114],"public":[117],"benchmark":[118],"dataset":[119],"17":[121],"patients,":[122],"each":[123],"40":[125],"volumes.":[127],"When":[128],"averaged":[129],"over":[130],"three":[131],"experiments,":[132],"highly":[134],"competitive":[135],"Dice":[136],"overlap":[137],"score":[138],"0.8688":[140],"sensitivity":[142],"0.8694":[144],"were":[145],"achieved.":[146],"As":[147],"spatiotemporal":[149],"method,":[151],"it":[152],"can":[153],"easily":[154],"migrate":[155],"to":[156],"other":[157],"datasets.":[158]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
