{"id":"https://openalex.org/W3130194259","doi":"https://doi.org/10.1117/12.2582333","title":"Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with signal intensity correction","display_name":"Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with signal intensity correction","publication_year":2021,"publication_date":"2021-02-12","ids":{"openalex":"https://openalex.org/W3130194259","doi":"https://doi.org/10.1117/12.2582333","mag":"3130194259"},"language":"en","primary_location":{"id":"doi:10.1117/12.2582333","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2582333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Computer-Aided Diagnosis","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/A5100607181","display_name":"Hyeonjin Kim","orcid":"https://orcid.org/0000-0003-2693-9983"},"institutions":[{"id":"https://openalex.org/I98600543","display_name":"Seoul Women's University","ror":"https://ror.org/04b2fhx54","country_code":"KR","type":"education","lineage":["https://openalex.org/I98600543"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyeonjin Kim","raw_affiliation_strings":["Seoul Women's Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Seoul Women's Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I98600543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035033921","display_name":"Helen Hong","orcid":"https://orcid.org/0000-0001-5044-7909"},"institutions":[{"id":"https://openalex.org/I98600543","display_name":"Seoul Women's University","ror":"https://ror.org/04b2fhx54","country_code":"KR","type":"education","lineage":["https://openalex.org/I98600543"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Helen Hong","raw_affiliation_strings":["Seoul Women's Univ. (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Seoul Women's Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I98600543"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019997070","display_name":"Dae Chul Jung","orcid":"https://orcid.org/0000-0001-5769-5083"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Dae Chul Jung","raw_affiliation_strings":["Yonsei Univ. College of Medicine (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Yonsei Univ. College of Medicine (Korea, Republic of)","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088514162","display_name":"Kidon Chang","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kidon Chang","raw_affiliation_strings":["Yonsei Univ. College of Medicine (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Yonsei Univ. College of Medicine (Korea, Republic of)","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080064863","display_name":"Koon Ho Rha","orcid":"https://orcid.org/0000-0001-8588-7584"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Koon Ho Rha","raw_affiliation_strings":["Yonsei Univ. College of Medicine (Korea, Republic of)"],"affiliations":[{"raw_affiliation_string":"Yonsei Univ. College of Medicine (Korea, Republic of)","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100607181"],"corresponding_institution_ids":["https://openalex.org/I98600543"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01987096,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"92","last_page":"92"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11885","display_name":"MRI in cancer diagnosis","score":0.9890000224113464,"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/T11885","display_name":"MRI in cancer diagnosis","score":0.9890000224113464,"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/T11174","display_name":"Pediatric Urology and Nephrology Studies","score":0.9854000210762024,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T12067","display_name":"Renal and Vascular Pathologies","score":0.9593999981880188,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.636346697807312},{"id":"https://openalex.org/keywords/parenchyma","display_name":"Parenchyma","score":0.5443321466445923},{"id":"https://openalex.org/keywords/coronal-plane","display_name":"Coronal plane","score":0.5202509164810181},{"id":"https://openalex.org/keywords/kidney","display_name":"Kidney","score":0.5107319355010986},{"id":"https://openalex.org/keywords/renal-function","display_name":"Renal function","score":0.499678373336792},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.47910478711128235},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3707238435745239},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3539579510688782},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.34208500385284424},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.18161755800247192},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.10501497983932495}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.636346697807312},{"id":"https://openalex.org/C196822366","wikidata":"https://www.wikidata.org/wiki/Q270524","display_name":"Parenchyma","level":2,"score":0.5443321466445923},{"id":"https://openalex.org/C13483470","wikidata":"https://www.wikidata.org/wiki/Q1470152","display_name":"Coronal plane","level":2,"score":0.5202509164810181},{"id":"https://openalex.org/C2780091579","wikidata":"https://www.wikidata.org/wiki/Q9377","display_name":"Kidney","level":2,"score":0.5107319355010986},{"id":"https://openalex.org/C159641895","wikidata":"https://www.wikidata.org/wiki/Q108377937","display_name":"Renal function","level":2,"score":0.499678373336792},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.47910478711128235},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3707238435745239},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3539579510688782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34208500385284424},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.18161755800247192},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.10501497983932495}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2582333","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2582333","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4298393804","https://openalex.org/W2374475973","https://openalex.org/W2373623138","https://openalex.org/W2358966148","https://openalex.org/W4284887224","https://openalex.org/W2085797056","https://openalex.org/W1516891752","https://openalex.org/W3154912244","https://openalex.org/W2041497154","https://openalex.org/W2983114262"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,75,103,199,223,234],"renal":[2,6,13,18,21,37,39,50,56,125,185,200,212,217,225],"parenchyma":[3,51,57,126,186,201,226],"responsible":[4],"for":[5,96,124,241],"function":[7,19,40,218],"is":[8,42,59,94,139,148,177,187],"necessary":[9],"to":[10,16,35,62,100,209,215],"evaluate":[11],"contralateral":[12,211],"hypertrophy":[14,213],"and":[15,89,164,214,237],"predict":[17,216],"after":[20,243],"partial":[22],"nephrectomy":[23],"(RPN).":[24],"Although":[25],"most":[26],"studies":[27],"have":[28],"segmented":[29,188],"the":[30,49,66,73,86,113,142,159,180,224],"kidney":[31,76,147,182],"on":[32,52,155,158,194,227],"CT":[33,235],"images":[34,229],"analyze":[36],"function,":[38],"analysis":[41],"required":[43],"without":[44,230],"radiation":[45,231],"exposure":[46,232],"by":[47,219],"segmenting":[48],"MR":[53,108,134,144,228],"images.":[54,135],"However,":[55],"segmentation":[58,127,152,191],"difficult":[60],"due":[61,99],"small":[63],"area":[64],"in":[65,72,132,141,179],"abdomen,":[67],"blurred":[68],"boundary,":[69],"large":[70],"variations":[71],"shape":[74],"among":[77],"patients,":[78],"similar":[79],"intensities":[80],"with":[81,112,128],"nearby":[82],"organs":[83],"such":[84],"as":[85],"liver,":[87],"spleen":[88],"vessels.":[90],"Furthermore,":[91],"signal":[92,129,171],"intensity":[93,130,137,172],"different":[95],"each":[97,175],"data":[98,176],"a":[101,167],"lot":[102],"movement":[104],"when":[105,110],"taking":[106],"abdominal":[107,133],"even":[109],"photographed":[111],"same":[114],"device.":[115],"Therefore,":[116],"we":[117],"propose":[118],"cascaded":[119],"deep":[120],"convolutional":[121],"neural":[122],"network":[123,192],"correction":[131,173],"First,":[136],"normalization":[138],"performed":[140,178],"whole":[143],"image.":[145],"Second,":[146],"localized":[149,181],"using":[150,189],"2D":[151],"networks":[153],"based":[154,193],"attention":[156],"UNet":[157],"axial,":[160],"coronal,":[161],"sagittal":[162],"planes":[163],"combining":[165],"through":[166],"majority":[168],"voting.":[169],"Third,":[170],"between":[174],"area.":[183],"Finally,":[184],"3D":[190],"UNet++.":[195],"The":[196],"average":[197],"DSC":[198],"was":[202],"91.57%.":[203],"Our":[204],"method":[205],"can":[206,238],"be":[207],"used":[208],"assess":[210],"measuring":[220],"volume":[221],"change":[222],"instead":[233],"images,":[236],"establish":[239],"basis":[240],"treatment":[242],"RPN.":[244]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}