{"id":"https://openalex.org/W3127938393","doi":"https://doi.org/10.1109/isbi48211.2021.9433848","title":"Learning Multi-Modal Volumetric Prostate Registration With Weak Inter-Subject Spatial Correspondence","display_name":"Learning Multi-Modal Volumetric Prostate Registration With Weak Inter-Subject Spatial Correspondence","publication_year":2021,"publication_date":"2021-04-13","ids":{"openalex":"https://openalex.org/W3127938393","doi":"https://doi.org/10.1109/isbi48211.2021.9433848","mag":"3127938393"},"language":"en","primary_location":{"id":"doi:10.1109/isbi48211.2021.9433848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi48211.2021.9433848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2102.04938","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010443783","display_name":"Oleksii Bashkanov","orcid":"https://orcid.org/0000-0001-9199-7333"},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Oleksii Bashkanov","raw_affiliation_strings":["Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany","institution_ids":["https://openalex.org/I4210138551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101797636","display_name":"Anneke Meyer","orcid":"https://orcid.org/0000-0002-7477-3175"},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Anneke Meyer","raw_affiliation_strings":["Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany","institution_ids":["https://openalex.org/I4210138551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112493885","display_name":"Daniel Schindele","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Daniel Schindele","raw_affiliation_strings":["Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany","University Hospital Magdeburg,Clinic of Urology and Pediatric Urology,Germany"],"affiliations":[{"raw_affiliation_string":"Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University Hospital Magdeburg,Clinic of Urology and Pediatric Urology,Germany","institution_ids":["https://openalex.org/I4210138551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079462263","display_name":"Martin Schostak","orcid":"https://orcid.org/0000-0002-8717-2096"},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Schostak","raw_affiliation_strings":["Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany","University Hospital Magdeburg,Clinic of Urology and Pediatric Urology,Germany"],"affiliations":[{"raw_affiliation_string":"Clinic of Urology and Pediatric Urology, University Hospital Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University Hospital Magdeburg,Clinic of Urology and Pediatric Urology,Germany","institution_ids":["https://openalex.org/I4210138551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109243131","display_name":"Klaus T\u00f6nnies","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Klaus-Dietz Tonnies","raw_affiliation_strings":["Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany","institution_ids":["https://openalex.org/I4210138551"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032675715","display_name":"Christian Hansen","orcid":"https://orcid.org/0000-0002-5734-7529"},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christian Hansen","raw_affiliation_strings":["Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany","institution_ids":["https://openalex.org/I4210138551"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070560950","display_name":"Marko Rak","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138551","display_name":"University Hospital Magdeburg","ror":"https://ror.org/03m04df46","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210138551"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Marko Rak","raw_affiliation_strings":["Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany"],"affiliations":[{"raw_affiliation_string":"Faculty of Computer Science & Research Campus STIMULATE, University of Magdeburg, Germany","institution_ids":["https://openalex.org/I4210138551"]},{"raw_affiliation_string":"University of Magdeburg,Faculty of Computer Science & Research Campus STIMULATE,Germany","institution_ids":["https://openalex.org/I4210138551"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5010443783"],"corresponding_institution_ids":["https://openalex.org/I4210138551"],"apc_list":null,"apc_paid":null,"fwci":0.1938,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.45791802,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1817","last_page":"1821"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9986000061035156,"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":0.9986000061035156,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9983999729156494,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9926000237464905,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7537401914596558},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7253451347351074},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.7107700705528259},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6858376860618591},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6273434162139893},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.5918586850166321},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5802886486053467},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5648051500320435},{"id":"https://openalex.org/keywords/transformation","display_name":"Transformation (genetics)","score":0.5575604438781738},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5054488182067871},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4919399917125702},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.49052995443344116},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.47768646478652954},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4657168388366699},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.2902766168117523},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24359411001205444},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20784980058670044}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7537401914596558},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253451347351074},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.7107700705528259},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6858376860618591},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6273434162139893},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.5918586850166321},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5802886486053467},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5648051500320435},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.5575604438781738},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5054488182067871},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4919399917125702},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.49052995443344116},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.47768646478652954},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4657168388366699},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2902766168117523},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24359411001205444},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20784980058670044},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/isbi48211.2021.9433848","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi48211.2021.9433848","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2102.04938","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.04938","pdf_url":"https://arxiv.org/pdf/2102.04938","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3127938393","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2102.04938","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2102.04938","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2102.04938","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2102.04938","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2102.04938","pdf_url":"https://arxiv.org/pdf/2102.04938","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W3127938393.pdf"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W2026616100","https://openalex.org/W2113576511","https://openalex.org/W2464708700","https://openalex.org/W2753461941","https://openalex.org/W2755930428","https://openalex.org/W2767463438","https://openalex.org/W2806236206","https://openalex.org/W2808034017","https://openalex.org/W2816936748","https://openalex.org/W2910741422","https://openalex.org/W2937483840","https://openalex.org/W2941137223","https://openalex.org/W3081623163","https://openalex.org/W3098315580","https://openalex.org/W3099933016","https://openalex.org/W3102875249","https://openalex.org/W6744542874"],"related_works":["https://openalex.org/W3165038731","https://openalex.org/W2753461941","https://openalex.org/W2963341239","https://openalex.org/W3090129185","https://openalex.org/W2074589309","https://openalex.org/W2979786163","https://openalex.org/W2096104495","https://openalex.org/W3131227683","https://openalex.org/W2890374903","https://openalex.org/W1626045299","https://openalex.org/W3135786805","https://openalex.org/W3150043901","https://openalex.org/W2883432564","https://openalex.org/W1738937218","https://openalex.org/W3169466694","https://openalex.org/W2973464190","https://openalex.org/W1766667371","https://openalex.org/W3203950382","https://openalex.org/W3191281636","https://openalex.org/W2980226202"],"abstract_inverted_index":{"Recent":[0],"studies":[1],"demonstrated":[2],"the":[3,12,67,82,86,101,112,142,146,150,154,166,182],"eligibility":[4],"of":[5,38,114],"convolutional":[6],"neural":[7,143],"networks":[8],"(CNNs)":[9],"for":[10,26,145],"solving":[11],"image":[13],"registration":[14,178],"problem.":[15],"CNNs":[16],"enable":[17],"faster":[18],"transformation":[19,127],"estimation":[20],"and":[21,55,100,116,125],"greater":[22],"generalization":[23],"capability":[24],"needed":[25],"better":[27],"support":[28],"during":[29],"medical":[30],"interventions.":[31],"Conventional":[32],"fully-supervised":[33],"training":[34],"requires":[35],"a":[36,52,76,122,131],"lot":[37],"high-quality":[39],"ground":[40,79],"truth":[41,80],"data":[42],"such":[43],"as":[44],"voxel-to-voxel":[45],"transformations,":[46],"which":[47,65,157],"typically":[48],"are":[49,75],"attained":[50],"in":[51,121,153],"too":[53],"tedious":[54],"error-prone":[56],"manner.":[57],"In":[58],"our":[59],"work,":[60],"we":[61,89,136,176],"use":[62],"weakly-supervised":[63],"learning,":[64],"optimizes":[66],"model":[68],"indirectly":[69],"only":[70],"via":[71],"segmentation":[72,92],"masks":[73],"that":[74,111],"more":[77,123],"accessible":[78],"than":[81],"deformation":[83],"fields.":[84],"Concerning":[85],"weak":[87],"supervision,":[88],"investigate":[90],"two":[91],"similarity":[93,97,102,118],"measures:":[94],"multiscale":[95],"Dice":[96],"coefficient":[98],"(mDSC)":[99],"between":[103],"segmentation-derived":[104],"signed":[105],"distance":[106],"maps":[107],"(SDMs).":[108],"We":[109],"show":[110],"combination":[113],"mDSC":[115],"SDM":[117],"measures":[119],"results":[120],"accurate":[124],"natural":[126],"pattern":[128],"together":[129],"with":[130],"stronger":[132],"gradient":[133],"coverage.":[134],"Furthermore,":[135],"introduce":[137],"an":[138],"auxiliary":[139],"input":[140],"to":[141,181],"network":[144],"prior":[147],"information":[148],"about":[149],"prostate":[151,174],"location":[152],"MR":[155],"sequence,":[156],"mostly":[158],"is":[159],"available":[160],"preoperatively.":[161],"This":[162],"approach":[163],"significantly":[164],"outperforms":[165],"standard":[167],"two-input":[168],"models.":[169],"With":[170],"weakly":[171],"labelled":[172],"MR-TRUS":[173],"data,":[175],"showed":[177],"quality":[179],"comparable":[180],"state-of-the-art":[183],"deep":[184],"learning-based":[185],"method.":[186]},"counts_by_year":[{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-21T08:13:44.787528","created_date":"2022-07-25T00:00:00"}