{"id":"https://openalex.org/W2088108868","doi":"https://doi.org/10.1109/icsai.2014.7009387","title":"Efficient deformable model with sparse shape composition prior on compromised right lung segmentation in CT","display_name":"Efficient deformable model with sparse shape composition prior on compromised right lung segmentation in CT","publication_year":2014,"publication_date":"2014-11-01","ids":{"openalex":"https://openalex.org/W2088108868","doi":"https://doi.org/10.1109/icsai.2014.7009387","mag":"2088108868"},"language":"en","primary_location":{"id":"doi:10.1109/icsai.2014.7009387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2014.7009387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014)","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/A5026395073","display_name":"Jinghao Zhou","orcid":"https://orcid.org/0000-0002-7744-0405"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jinghao Zhou","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047637389","display_name":"G Lasio","orcid":"https://orcid.org/0000-0001-6366-2595"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Giovanni Lasio","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032904110","display_name":"Baoshe Zhang","orcid":"https://orcid.org/0000-0003-1605-070X"},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Baoshe Zhang","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108601845","display_name":"K Prado","orcid":null},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karl Prado","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113523411","display_name":"W D\u02c8Souza","orcid":null},"institutions":[{"id":"https://openalex.org/I126744593","display_name":"University of Maryland, Baltimore","ror":"https://ror.org/04rq5mt64","country_code":"US","type":"education","lineage":["https://openalex.org/I126744593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Warren D'Souza","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, Baltimore, MD, USA","institution_ids":["https://openalex.org/I126744593"]},{"raw_affiliation_string":"Department of Radiation Oncology University of Maryland School of Medicine Baltimore MD USA","institution_ids":["https://openalex.org/I126744593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077384503","display_name":"Zhennan Yan","orcid":"https://orcid.org/0000-0001-7128-1696"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhennan Yan","raw_affiliation_strings":["Department of Computer Science, Rutgers, the State University of New Jersey, Piscataway, NJ, USA","Department of Computer Science\u2019 Rutgers, The State University of New Jersey, Piscataway, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers, the State University of New Jersey, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]},{"raw_affiliation_string":"Department of Computer Science\u2019 Rutgers, The State University of New Jersey, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109600054","display_name":"Dimitris Metaxas","orcid":null},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dimitris Metaxas","raw_affiliation_strings":["Department of Computer Science, Rutgers, the State University of New Jersey, Piscataway, NJ, USA","Department of Computer Science\u2019 Rutgers, The State University of New Jersey, Piscataway, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Rutgers, the State University of New Jersey, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]},{"raw_affiliation_string":"Department of Computer Science\u2019 Rutgers, The State University of New Jersey, Piscataway, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5026395073"],"corresponding_institution_ids":["https://openalex.org/I126744593"],"apc_list":null,"apc_paid":null,"fwci":0.2612,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63078223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"764","last_page":"768"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9984999895095825,"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"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9984999895095825,"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/T10202","display_name":"Lung Cancer Diagnosis and Treatment","score":0.9958999752998352,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9951000213623047,"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/segmentation","display_name":"Segmentation","score":0.7485228776931763},{"id":"https://openalex.org/keywords/lung","display_name":"Lung","score":0.6195191740989685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5381032228469849},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5311989188194275},{"id":"https://openalex.org/keywords/active-shape-model","display_name":"Active shape model","score":0.5051658749580383},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.47697409987449646},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4757477939128876},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.43156570196151733},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43127188086509705},{"id":"https://openalex.org/keywords/lung-cancer","display_name":"Lung cancer","score":0.4245925545692444},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3503623604774475},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2596210241317749},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.07488003373146057},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.06897959113121033}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7485228776931763},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.6195191740989685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5381032228469849},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5311989188194275},{"id":"https://openalex.org/C129641003","wikidata":"https://www.wikidata.org/wiki/Q267189","display_name":"Active shape model","level":3,"score":0.5051658749580383},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.47697409987449646},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4757477939128876},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.43156570196151733},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43127188086509705},{"id":"https://openalex.org/C2776256026","wikidata":"https://www.wikidata.org/wiki/Q47912","display_name":"Lung cancer","level":2,"score":0.4245925545692444},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3503623604774475},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2596210241317749},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.07488003373146057},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.06897959113121033}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icsai.2014.7009387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icsai.2014.7009387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6800000071525574,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W33507944","https://openalex.org/W58162092","https://openalex.org/W1519123984","https://openalex.org/W1556218437","https://openalex.org/W1576409155","https://openalex.org/W1605635602","https://openalex.org/W1970089523","https://openalex.org/W1998790582","https://openalex.org/W2016672701","https://openalex.org/W2023695105","https://openalex.org/W2024798729","https://openalex.org/W2027091505","https://openalex.org/W2030345790","https://openalex.org/W2035107885","https://openalex.org/W2036390026","https://openalex.org/W2038952578","https://openalex.org/W2049266121","https://openalex.org/W2051694134","https://openalex.org/W2059583146","https://openalex.org/W2063598448","https://openalex.org/W2069303738","https://openalex.org/W2093995851","https://openalex.org/W2094540743","https://openalex.org/W2106736707","https://openalex.org/W2109465169","https://openalex.org/W2110872054","https://openalex.org/W2111033515","https://openalex.org/W2111042557","https://openalex.org/W2116040950","https://openalex.org/W2119360734","https://openalex.org/W2120621413","https://openalex.org/W2122692815","https://openalex.org/W2128246358","https://openalex.org/W2129417697","https://openalex.org/W2131251288","https://openalex.org/W2133059825","https://openalex.org/W2140775860","https://openalex.org/W2141453390","https://openalex.org/W2145332994","https://openalex.org/W2147427282","https://openalex.org/W2147484997","https://openalex.org/W2156061276","https://openalex.org/W2160962723","https://openalex.org/W2161912327","https://openalex.org/W2164774194","https://openalex.org/W2167803594","https://openalex.org/W2169620647","https://openalex.org/W2170997483","https://openalex.org/W2172250250","https://openalex.org/W2178973575","https://openalex.org/W4229921563","https://openalex.org/W6633216557","https://openalex.org/W6636021279","https://openalex.org/W6657562319","https://openalex.org/W7027716524","https://openalex.org/W7052257525"],"related_works":["https://openalex.org/W3197954266","https://openalex.org/W4389009345","https://openalex.org/W3047746737","https://openalex.org/W3021454079","https://openalex.org/W1522196789","https://openalex.org/W4287691568","https://openalex.org/W4220718606","https://openalex.org/W2085143385","https://openalex.org/W2005489729","https://openalex.org/W4200158463"],"abstract_inverted_index":{"We":[0],"developed":[1],"an":[2],"automated":[3],"lung":[4,20,35,41,82,87,90,201,204],"segmentation":[5,126,169],"method,":[6],"which":[7],"uses":[8],"deformable":[9,68,97],"model":[10,60,69,115,180],"with":[11,18,22,34,70,88,135,143,153,171,175],"sparse":[12,57],"shape":[13,58,179],"composition":[14,59],"prior":[15,72],"for":[16,32,116,200],"patients":[17,33],"compromised":[19,89],"volumes":[21,91],"severe":[23],"pathologies":[24],"in":[25,43,113,189,192],"CT.":[26],"Fifteen":[27],"thoracic":[28,193],"computed":[29,194],"tomography":[30,195],"scans":[31],"tumors":[36],"were":[37],"collected":[38],"and":[39,105,118,146,158,196],"reference":[40],"ROIs":[42],"each":[44],"scan":[45],"was":[46],"manually":[47],"segmented":[48,80,93],"to":[49,77],"assess":[50],"the":[51,54,67,78,85,95],"performance":[52],"of":[53,124,132,140,150],"method.":[55],"First,":[56],"is":[61,92],"constructed":[62],"using":[63,94],"training":[64],"dataset.":[65],"Next,":[66],"SSC":[71],"will":[73,186],"be":[74,187],"initialized":[75],"according":[76],"rough":[79],"right":[81,86],"ROI.":[83],"Then,":[84],"robust":[96,119,177],"model.":[98],"Energy":[99],"terms":[100],"from":[101],"ROI":[102,107],"edge":[103],"potential":[104,110],"interior":[106],"region":[108],"based":[109],"are":[111],"combined":[112],"this":[114],"accurate":[117],"segmentation.":[120],"The":[121,156,183],"quantitative":[122,159],"results":[123],"our":[125,163],"method":[127,165,181,185],"achieved":[128],"mean":[129,138,147],"dice":[130],"score":[131],"(0.86,":[133],"0.97)":[134],"95%":[136,144,154],"CI,":[137,145],"accuracy":[139,170],"(0.93,":[141],"0.98)":[142],"relative":[148],"error":[149],"(0.07,":[151],"0.17)":[152],"CI.":[155],"qualitative":[157],"comparisons":[160],"show":[161],"that":[162],"proposed":[164,184],"can":[166],"achieve":[167],"better":[168],"less":[172],"variance":[173],"compared":[174],"a":[176],"active":[178],"(RASM).":[182],"useful":[188],"radiotherapy":[190],"assessment":[191],"image":[197],"analysis":[198],"applications":[199],"nodule":[202],"or":[203],"cancer":[205],"diagnosis.":[206]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
