{"id":"https://openalex.org/W2905562891","doi":"https://doi.org/10.1109/lra.2019.2896518","title":"Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation","display_name":"Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation","publication_year":2019,"publication_date":"2019-01-30","ids":{"openalex":"https://openalex.org/W2905562891","doi":"https://doi.org/10.1109/lra.2019.2896518","mag":"2905562891"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2019.2896518","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2896518","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-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/A5030025128","display_name":"Xiao-Yun Zhou","orcid":"https://orcid.org/0000-0001-7886-8596"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Xiao-Yun Zhou","raw_affiliation_strings":["Hamlyn Centre for Robotic Surgery, Imperial College London, London, U.K"],"affiliations":[{"raw_affiliation_string":"Hamlyn Centre for Robotic Surgery, Imperial College London, London, U.K","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085036036","display_name":"Guang\u2010Zhong Yang","orcid":"https://orcid.org/0000-0003-4060-4020"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Guang-Zhong Yang","raw_affiliation_strings":["Hamlyn Centre for Robotic Surgery, Imperial College London, London, U.K"],"affiliations":[{"raw_affiliation_string":"Hamlyn Centre for Robotic Surgery, Imperial College London, London, U.K","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030025128"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":13.0217,"has_fulltext":false,"cited_by_count":107,"citation_normalized_percentile":{"value":0.99151006,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"4","issue":"2","first_page":"1792","last_page":"1799"},"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":0.9993000030517578,"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":0.9993000030517578,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9975000023841858,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9975000023841858,"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/segmentation","display_name":"Segmentation","score":0.824062705039978},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.7476897239685059},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7299690842628479},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6557395458221436},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6226370334625244},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5835000276565552},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4546535313129425}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.824062705039978},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.7476897239685059},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7299690842628479},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6557395458221436},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6226370334625244},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5835000276565552},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4546535313129425},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2019.2896518","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2019.2896518","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1623289732","display_name":null,"funder_award_id":"EP/L020688/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5703964369","display_name":null,"funder_award_id":"EP/H009744/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1836465849","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2105789358","https://openalex.org/W2284050935","https://openalex.org/W2296621185","https://openalex.org/W2340897893","https://openalex.org/W2412453619","https://openalex.org/W2419312574","https://openalex.org/W2592929672","https://openalex.org/W2594833348","https://openalex.org/W2604319603","https://openalex.org/W2760344638","https://openalex.org/W2773060975","https://openalex.org/W2792023360","https://openalex.org/W2792101839","https://openalex.org/W2795136709","https://openalex.org/W2803808038","https://openalex.org/W2804047627","https://openalex.org/W2889158831","https://openalex.org/W2902302607","https://openalex.org/W2914821433","https://openalex.org/W2919115771","https://openalex.org/W2962839335","https://openalex.org/W2963685250","https://openalex.org/W2963702144","https://openalex.org/W3098080070","https://openalex.org/W3101907273","https://openalex.org/W4205182665","https://openalex.org/W4250482878","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6695676441","https://openalex.org/W6697620625","https://openalex.org/W6717327506","https://openalex.org/W6734215995","https://openalex.org/W6751546485","https://openalex.org/W6756113511","https://openalex.org/W6780226713","https://openalex.org/W6785540970","https://openalex.org/W6805630560"],"related_works":["https://openalex.org/W2591697403","https://openalex.org/W2953716828","https://openalex.org/W2904857019","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4315434538"],"abstract_inverted_index":{"Two-dimensional":[0],"(2-D)":[1],"biomedical":[2,42,117,125,168,200],"semantic":[3,43,118,126,169,201],"segmentation":[4,44,119,127,142,170],"is":[5,45,56,77,88,105,204],"important":[6],"for":[7,41,82,151,167,198,223],"robotic":[8],"vision":[9],"in":[10,25,37,123,128,195],"surgery.":[11],"Segmentation":[12],"methods":[13,24],"based":[14],"on":[15,161],"deep":[16],"convolutional":[17,54],"neural":[18],"network":[19],"(DCNN)":[20],"can":[21],"out-perform":[22],"conventional":[23],"terms":[26],"of":[27,32,53,62,232],"both":[28,66],"accuracy":[29,249],"and":[30,70,87,94,109,137,143,191,218,260],"levels":[31],"automation.":[33],"One":[34],"common":[35],"issue":[36],"training":[38,52,68],"a":[39,239],"DCNN":[40,209],"the":[46,51,59,67,78,174,207,214,224,233],"internal":[47,84],"covariate":[48,85],"shift":[49,86],"where":[50],"kernels":[55],"encumbered":[57],"by":[58,177],"distribution":[60],"change":[61],"input":[63],"features,":[64],"hence":[65],"speed":[69],"performance":[71],"are":[72,193,221,258],"decreased.":[73],"Batch":[74],"normalization":[75,92,96,103,175,187],"(BN)":[76],"first":[79],"proposed":[80,106],"method":[81,176],"addressing":[83],"widely":[89],"used.":[90],"Instance":[91],"(IN)":[93],"layer":[95],"(LN)":[97],"have":[98],"also":[99],"been":[100,113],"proposed.":[101],"Group":[102],"(GN)":[104],"more":[107],"recently":[108],"has":[110],"not":[111],"yet":[112],"applied":[114],"to":[115],"2-D":[116,199],"(GN":[120],"was":[121],"used":[122,222],"3-D":[124],"[P.-Y.":[129],"Kao,":[130],"T.":[131],"Ngo,":[132],"A.":[133],"Zhang,":[134],"J.":[135],"Chen,":[136],"B.":[138],"Manjunath,":[139],"Brain":[140],"tumor":[141],"tractographic":[144],"feature":[145,234],"extraction":[146],"from":[147,253],"structural":[148],"MR":[149],"images":[150],"overall":[152],"survival":[153],"prediction":[154],"2018,":[155],"arXiv:1807.07716],":[156],"however,":[157],"no":[158],"specific":[159],"validations":[160],"GN":[162,192,237],"were":[163],"given).":[164],"Most":[165],"DCNNs":[166],"adopt":[171],"BN":[172],"as":[173,206],"default,":[178],"without":[179],"reviewing":[180],"its":[181],"performance.":[182],"In":[183],"this":[184],"letter,":[185],"four":[186],"methods-BN,":[188],"IN,":[189,244],"LN,":[190],"compared":[194],"details,":[196],"specifically":[197],"segmentation.":[202],"U-Net":[203],"adopted":[205],"basic":[208],"structure.":[210],"Three":[211],"datasets":[212],"regarding":[213],"right":[215],"ventricle,":[216],"aorta,":[217],"left":[219],"ventricle":[220],"validation.":[225],"The":[226],"results":[227],"show":[228],"that":[229],"detailed":[230],"subdivision":[231],"map,":[235],"i.e.,":[236],"with":[238],"large":[240],"group":[241],"number":[242],"or":[243],"achieves":[245],"higher":[246],"accuracy.":[247],"This":[248],"improvement":[250],"mainly":[251],"comes":[252],"better":[254],"model":[255],"generalization.":[256],"Codes":[257],"uploaded":[259],"maintained":[261],"at":[262],"Xiao-Yun":[263],"Zhou's":[264],"Github.":[265]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":17},{"year":2019,"cited_by_count":14}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
