{"id":"https://openalex.org/W3009170070","doi":"https://doi.org/10.1109/isbi45749.2020.9098620","title":"Volumetric Landmark Detection with a Multi-Scale Shift Equivariant Neural Network","display_name":"Volumetric Landmark Detection with a Multi-Scale Shift Equivariant Neural Network","publication_year":2020,"publication_date":"2020-04-01","ids":{"openalex":"https://openalex.org/W3009170070","doi":"https://doi.org/10.1109/isbi45749.2020.9098620","mag":"3009170070","pmid":"https://pubmed.ncbi.nlm.nih.gov/38915907"},"language":"en","primary_location":{"id":"doi:10.1109/isbi45749.2020.9098620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi45749.2020.9098620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2003.01639","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102802608","display_name":"Tianyu Ma","orcid":"https://orcid.org/0000-0003-1051-4614"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tianyu Ma","raw_affiliation_strings":["School of Electrical and Computer Engineering; and Meinig School of Biomedical Engineering, Cornell University","[School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering, Cornell University]"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering; and Meinig School of Biomedical Engineering, Cornell University","institution_ids":[]},{"raw_affiliation_string":"[School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering, Cornell University]","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059916379","display_name":"Ajay Gupta","orcid":"https://orcid.org/0000-0003-2816-0340"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ajay Gupta","raw_affiliation_strings":["Department of Radiology, Weill Cornell Medical College","Weill Cornell Medical College, Department of Radiology"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Weill Cornell Medical College","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Weill Cornell Medical College, Department of Radiology","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025877936","display_name":"Mert R. Sabuncu","orcid":"https://orcid.org/0000-0002-7068-719X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mert R. Sabuncu","raw_affiliation_strings":["Department of Radiology, Weill Cornell Medical College","School of Electrical and Computer Engineering; and Meinig School of Biomedical Engineering, Cornell University","Weill Cornell Medical College, Department of Radiology"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Weill Cornell Medical College","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"School of Electrical and Computer Engineering; and Meinig School of Biomedical Engineering, Cornell University","institution_ids":[]},{"raw_affiliation_string":"Weill Cornell Medical College, Department of Radiology","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102802608"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01953362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2020","issue":null,"first_page":"981","last_page":"985"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9987999796867371,"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":0.9987999796867371,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9983999729156494,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.991100013256073,"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/landmark","display_name":"Landmark","score":0.9399799108505249},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7371073365211487},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7047117352485657},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6166188716888428},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5881209969520569},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5782333016395569},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5055997371673584},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4786223769187927},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47214558720588684},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4437263607978821},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41722047328948975},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.10906800627708435},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08998915553092957}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.9399799108505249},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7371073365211487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7047117352485657},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6166188716888428},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5881209969520569},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5782333016395569},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5055997371673584},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4786223769187927},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47214558720588684},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4437263607978821},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41722047328948975},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.10906800627708435},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08998915553092957},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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":7,"locations":[{"id":"doi:10.1109/isbi45749.2020.9098620","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi45749.2020.9098620","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},{"id":"pmid:38915907","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38915907","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. IEEE International Symposium on Biomedical Imaging","raw_type":null},{"id":"pmh:oai:arXiv.org:2003.01639","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.01639","pdf_url":"https://arxiv.org/pdf/2003.01639","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:3009170070","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2003.01639","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":"pmh:oai:pubmedcentral.nih.gov:11194796","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11194796","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Proc IEEE Int Symp Biomed Imaging","raw_type":"Text"},{"id":"doi:10.48550/arxiv.2003.01639","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2003.01639","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-journal"},{"id":"doi:10.17023/03dq-0124","is_oa":true,"landing_page_url":"https://doi.org/10.17023/03dq-0124","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":[],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2003.01639","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2003.01639","pdf_url":"https://arxiv.org/pdf/2003.01639","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":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1573412753","https://openalex.org/W1901129140","https://openalex.org/W1976948919","https://openalex.org/W2095705004","https://openalex.org/W2135132101","https://openalex.org/W2325168940","https://openalex.org/W2341528187","https://openalex.org/W2399244897","https://openalex.org/W2464708700","https://openalex.org/W2525974879","https://openalex.org/W2754156725","https://openalex.org/W2797997321","https://openalex.org/W2922479016","https://openalex.org/W2950754204","https://openalex.org/W2951655307","https://openalex.org/W2963377935","https://openalex.org/W2963881378","https://openalex.org/W2964059111","https://openalex.org/W3101998545","https://openalex.org/W6617145748","https://openalex.org/W6639824700","https://openalex.org/W6674330103","https://openalex.org/W6701270854","https://openalex.org/W6712205131","https://openalex.org/W6718752587","https://openalex.org/W6750171631"],"related_works":["https://openalex.org/W3114174666","https://openalex.org/W2260428369","https://openalex.org/W3126655046","https://openalex.org/W3048839720","https://openalex.org/W3162341349","https://openalex.org/W2999981064","https://openalex.org/W3157615508","https://openalex.org/W3110517830","https://openalex.org/W2804654955","https://openalex.org/W2130735183","https://openalex.org/W2786069628","https://openalex.org/W1556839878","https://openalex.org/W3010668292","https://openalex.org/W3182891170","https://openalex.org/W3150429754","https://openalex.org/W3104993299","https://openalex.org/W3034478221","https://openalex.org/W3044643673","https://openalex.org/W3161606608","https://openalex.org/W3155223001"],"abstract_inverted_index":{"Deep":[0],"neural":[1,44],"networks":[2],"yield":[3],"promising":[4],"results":[5],"in":[6,25,53,77],"a":[7,64,95,122,157],"wide":[8],"range":[9],"of":[10,41,59,83,85,89,130,167],"computer":[11],"vision":[12],"applications,":[13],"including":[14],"landmark":[15,23,75,92],"detection.":[16],"A":[17],"major":[18],"challenge":[19],"for":[20,146],"accurate":[21],"anatomical":[22],"detection":[24,76,93,150],"volumetric":[26],"images":[27],"such":[28],"as":[29],"clinical":[30],"CT":[31,153],"scans":[32],"is":[33],"that":[34,70,112,126],"large-scale":[35],"data":[36],"often":[37],"constrain":[38],"the":[39,42,57,60,128,131],"capacity":[40],"employed":[43],"network":[45],"architecture":[46,81],"due":[47],"to":[48,105,136],"GPU":[49],"memory":[50],"limitations,":[51],"which":[52,90],"turn":[54],"can":[55,115],"limit":[56],"precision":[58],"output.":[61],"We":[62,119,142],"propose":[63],"multi-scale,":[65],"end-to-end":[66],"deep":[67],"learning":[68],"method":[69,145],"achieves":[71],"fast":[72],"and":[73,133,155],"memory-efficient":[74],"3D":[78],"images.":[79],"Our":[80],"consists":[82],"blocks":[84,100],"shift-equivariant":[86],"networks,":[87],"each":[88],"performs":[91],"at":[94,139],"different":[96],"spatial":[97],"scale.":[98],"These":[99],"are":[101],"connected":[102],"from":[103],"coarse":[104],"fine-scale,":[106],"with":[107,162],"differentiable":[108],"resampling":[109],"layers,":[110],"so":[111],"all":[113],"levels":[114],"be":[116],"trained":[117],"together.":[118],"also":[120],"present":[121],"noise":[123],"injection":[124],"strategy":[125],"increases":[127],"robustness":[129],"model":[132],"allows":[134],"us":[135],"quantify":[137],"uncertainty":[138],"test":[140],"time.":[141],"evaluate":[143],"our":[144],"carotid":[147],"artery":[148],"bifurcations":[149],"on":[151],"263":[152],"volumes":[154],"achieve":[156],"better":[158],"than":[159],"state-of-the-art":[160],"accuracy":[161],"mean":[163],"Euclidean":[164],"distance":[165],"error":[166],"2.81mm.":[168]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
