{"id":"https://openalex.org/W4285347520","doi":"https://doi.org/10.1109/aipr52630.2021.9762167","title":"A Deep Learning Approach for Contour Interpolation","display_name":"A Deep Learning Approach for Contour Interpolation","publication_year":2021,"publication_date":"2021-10-12","ids":{"openalex":"https://openalex.org/W4285347520","doi":"https://doi.org/10.1109/aipr52630.2021.9762167"},"language":"en","primary_location":{"id":"doi:10.1109/aipr52630.2021.9762167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr52630.2021.9762167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5085490099","display_name":"Chenxi Zhao","orcid":"https://orcid.org/0000-0002-4031-355X"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenxi Zhao","raw_affiliation_strings":["University of Missouri,EECS Department,Columbia,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,EECS Department,Columbia,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101430356","display_name":"Ye Duan","orcid":"https://orcid.org/0000-0002-1166-7703"},"institutions":[{"id":"https://openalex.org/I76835614","display_name":"University of Missouri","ror":"https://ror.org/02ymw8z06","country_code":"US","type":"education","lineage":["https://openalex.org/I76835614"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ye Duan","raw_affiliation_strings":["University of Missouri,EECS Deparment,Columbia,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Missouri,EECS Deparment,Columbia,USA","institution_ids":["https://openalex.org/I76835614"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102012443","display_name":"Deshan Yang","orcid":"https://orcid.org/0000-0002-2568-247X"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deshan Yang","raw_affiliation_strings":["Washington University,Department of Radiation Oncology,St. Louis,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Washington University,Department of Radiation Oncology,St. Louis,USA","institution_ids":["https://openalex.org/I204465549"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.097,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.44618635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965999722480774,"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":0.9965999722480774,"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":0.9955000281333923,"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"}},{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.7749093770980835},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7256150245666504},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7093613147735596},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5783416628837585},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5632658004760742},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.5520496964454651},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5140167474746704},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47628775238990784},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4212720990180969},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4163110852241516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34155958890914917},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15069440007209778},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.14449745416641235}],"concepts":[{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.7749093770980835},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7256150245666504},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7093613147735596},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5783416628837585},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5632658004760742},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5520496964454651},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5140167474746704},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47628775238990784},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4212720990180969},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4163110852241516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34155958890914917},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15069440007209778},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.14449745416641235},{"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":1,"locations":[{"id":"doi:10.1109/aipr52630.2021.9762167","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr52630.2021.9762167","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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":14,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1966764112","https://openalex.org/W2001189086","https://openalex.org/W2012219217","https://openalex.org/W2057980469","https://openalex.org/W2156010062","https://openalex.org/W2532947464","https://openalex.org/W2915126261","https://openalex.org/W3129135075","https://openalex.org/W4205599099","https://openalex.org/W4309233581","https://openalex.org/W6641662656","https://openalex.org/W6805811239","https://openalex.org/W6977944434"],"related_works":["https://openalex.org/W3104750253","https://openalex.org/W3021239166","https://openalex.org/W4366341510","https://openalex.org/W2390936256","https://openalex.org/W2483429559","https://openalex.org/W3024479225","https://openalex.org/W3171371563","https://openalex.org/W4379141755","https://openalex.org/W3133954817","https://openalex.org/W3003847115"],"abstract_inverted_index":{"Contour":[0],"interpolation":[1,16,79],"is":[2],"an":[3],"important":[4],"tool":[5],"to":[6,61,139,157,176],"expedite":[7,177],"manual":[8,34,181],"segmentation":[9,182],"of":[10,45,85,92,100,180,183],"anatomical":[11],"structures.":[12,51],"The":[13,76],"conventional":[14,111,149],"shape-based":[15],"(SBI)":[17],"algorithm,":[18],"based":[19],"on":[20,96],"distance":[21],"map":[22],"calculation":[23],"then":[24],"interpolation,":[25,69],"often":[26],"performs":[27],"sub-optimally":[28],"when":[29],"the":[30,40,49,63,107,110,120,123,135,148,160,178,188],"two":[31],"adjacent":[32],"to-be-interpolated":[33],"contours":[35],"differ":[36],"dramatically,":[37],"especially":[38,70],"near":[39],"superior":[41],"and":[42,47,65,87,116,126,131,144,185],"inferior":[43],"borders":[44],"organs":[46,184],"for":[48,67,71,169],"gastrointestinal":[50],"In":[52,104],"this":[53],"study,":[54],"we":[55],"present":[56],"a":[57,82,88,105],"deep":[58,77,136],"learning":[59],"solution":[60],"improve":[62],"robustness":[64],"accuracy":[66],"contour":[68,78],"these":[72],"historically":[73],"difficult":[74,121],"cases.":[75,171],"model":[80],"achieved":[81],"dice":[83,124],"score":[84,125],"0.95\u00b10.06":[86],"mean":[89],"DTA":[90,127],"value":[91,128],"1.08\u00b12.31":[93],"mm,":[94],"averaged":[95],"3167":[97],"testing":[98],"cases":[99],"all":[101,170],"16":[102],"organs.":[103],"comparison,":[106],"results":[108],"by":[109,134,147],"SBI":[112,150],"method":[113],"were":[114,129,163],"0.94\u00b10.08":[115],"1.50\u00b13.63mm,":[117],"respectively.":[118],"For":[119],"cases,":[122],"0.91\u00b10.09":[130],"1.71\u00b12.25":[132],"mm":[133,146],"model,":[137],"compared":[138],"0.":[140,142,167],"86\u00b1":[141],"13":[143],"3.42\u00b15.88":[145],"method.":[151],"A":[152],"student":[153],"t-test":[154],"was":[155],"applied":[156],"confirm":[158],"that":[159],"performance":[161],"improvements":[162],"statistically":[164],"significant":[165],"(p<":[166],"05)":[168],"It":[172],"could":[173],"be":[174],"useful":[175],"tasks":[179],"structures":[186],"in":[187],"medical":[189],"images.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}