{"id":"https://openalex.org/W2753006639","doi":"https://doi.org/10.1109/icip.2017.8296847","title":"Prostate detection and segmentation based on convolutional neural network and topological derivative","display_name":"Prostate detection and segmentation based on convolutional neural network and topological derivative","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2753006639","doi":"https://doi.org/10.1109/icip.2017.8296847","mag":"2753006639"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296847","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","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/A5006431171","display_name":"Choongsang Cho","orcid":"https://orcid.org/0000-0001-5952-5491"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Choongsang Cho","raw_affiliation_strings":["Korea Electronics Technology Institute, Seongnam, KR"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Electronics Technology Institute, Seongnam, KR","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101410938","display_name":"Young Han Lee","orcid":"https://orcid.org/0000-0001-8200-2867"},"institutions":[{"id":"https://openalex.org/I4210131650","display_name":"Korea Electronics Technology Institute","ror":"https://ror.org/039k6f508","country_code":"KR","type":"facility","lineage":["https://openalex.org/I2801339556","https://openalex.org/I4210089395","https://openalex.org/I4210131650"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young Han Lee","raw_affiliation_strings":["Artificial Intelligence Research Center, Korea Electronics Technology Institute, Seongnam, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Research Center, Korea Electronics Technology Institute, Seongnam, Korea","institution_ids":["https://openalex.org/I4210131650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028945187","display_name":"Sangkeun Lee","orcid":"https://orcid.org/0000-0001-6589-6774"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sangkeun Lee","raw_affiliation_strings":["Graduate School of Advanced Imaging Science, Multimedia & Film Chung-Ang University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Advanced Imaging Science, Multimedia & Film Chung-Ang University, Seoul, Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4619,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73242484,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"20","issue":null,"first_page":"3071","last_page":"3074"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9861999750137329,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9861999750137329,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9833999872207642,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9661999940872192,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7631711959838867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6406154036521912},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5518530011177063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5030185580253601},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4407910704612732},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.40795034170150757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11684665083885193},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.06799113750457764}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7631711959838867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6406154036521912},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5518530011177063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5030185580253601},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4407910704612732},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.40795034170150757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11684665083885193},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.06799113750457764}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296847","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296847","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1745334888","https://openalex.org/W1861492603","https://openalex.org/W1903029394","https://openalex.org/W1965705881","https://openalex.org/W1981114316","https://openalex.org/W2106033751","https://openalex.org/W2132116135","https://openalex.org/W2186094539","https://openalex.org/W2478917285","https://openalex.org/W2484736472","https://openalex.org/W2604790786","https://openalex.org/W6639102338"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4239306820","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W2947043951","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"The":[0,31],"topological":[1],"derivative":[2],"(TD)":[3],"for":[4,99,123],"shape":[5],"analysis":[6],"has":[7],"been":[8],"employed":[9,63],"in":[10,27,75],"image":[11,29,126],"segmentation,":[12],"and":[13,33,39,79],"machine":[14],"learning":[15],"schames":[16],"based":[17,55],"on":[18],"convolutional":[19],"neural":[20],"network":[21],"(CNN)":[22],"provide":[23],"the":[24,28,47,67,82,93,100,104,115],"high":[25],"performance":[26,98],"processing.":[30],"supervised":[32],"unsupervised":[34],"approaches":[35],"have":[36],"different":[37],"roles":[38],"advantages":[40],"according":[41],"to":[42,64,72],"their":[43],"concepts.":[44],"To":[45],"maximize":[46],"benefits":[48],"of":[49,69],"two":[50],"approaches,":[51],"we":[52,80,112],"propose":[53],"CNN-TD":[54],"segmentation":[56,60,102],"approach.":[57],"A":[58],"CNN-based":[59],"scheme":[61,95,117],"is":[62],"faithfully":[65],"consider":[66],"characteristics":[68],"an":[70],"object":[71],"be":[73,119],"segmented":[74],"a":[76,86,120],"given":[77],"image,":[78],"refine":[81],"CNN":[83],"results":[84,90,106],"using":[85],"TD-based":[87],"scheme.":[88],"Experimental":[89],"show":[91],"that":[92,114],"proposed":[94,116],"produces":[96],"better":[97],"prostate":[101],"than":[103],"refined":[105],"by":[107],"level":[108],"set-based":[109],"schemes.":[110],"Therefore,":[111],"believe":[113],"can":[118],"useful":[121],"tool":[122],"effective":[124],"medical":[125],"segmentation.":[127]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}