{"id":"https://openalex.org/W3088747006","doi":"https://doi.org/10.1145/3417188.3417191","title":"Combining Residual learning and U-Net for Hippocampus Segmentation of Brain MRI Volume Image","display_name":"Combining Residual learning and U-Net for Hippocampus Segmentation of Brain MRI Volume Image","publication_year":2020,"publication_date":"2020-07-10","ids":{"openalex":"https://openalex.org/W3088747006","doi":"https://doi.org/10.1145/3417188.3417191","mag":"3088747006"},"language":"en","primary_location":{"id":"doi:10.1145/3417188.3417191","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3417188.3417191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies (ICDLT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101773594","display_name":"Chao Jia","orcid":"https://orcid.org/0000-0001-8706-0935"},"institutions":[{"id":"https://openalex.org/I4400600917","display_name":"Guangzhou College of Commerce","ror":"https://ror.org/04f0j5d06","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600917"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Jia","raw_affiliation_strings":["Guangzhou College of Commerce, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou College of Commerce, Guangzhou, China","institution_ids":["https://openalex.org/I4400600917"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076406655","display_name":"Changrun Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4400600917","display_name":"Guangzhou College of Commerce","ror":"https://ror.org/04f0j5d06","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600917"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changrun Jia","raw_affiliation_strings":["Guangzhou College of Commerce, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou College of Commerce, Guangzhou, China","institution_ids":["https://openalex.org/I4400600917"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004850572","display_name":"Hailan Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I4400600917","display_name":"Guangzhou College of Commerce","ror":"https://ror.org/04f0j5d06","country_code":null,"type":"education","lineage":["https://openalex.org/I4400600917"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hailan Yu","raw_affiliation_strings":["Guangzhou College of Commerce, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Guangzhou College of Commerce, Guangzhou, China","institution_ids":["https://openalex.org/I4400600917"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4400600917"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"94","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9987000226974487,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9829000234603882,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7838754057884216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7373528480529785},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7189781665802002},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5929271578788757},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.590154230594635},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5498181581497192},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.5399278998374939},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5226473808288574},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.506445050239563},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5035483241081238},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.469951868057251},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.44705525040626526},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.41086938977241516},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3552793264389038},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.06671872735023499}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7838754057884216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7373528480529785},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7189781665802002},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5929271578788757},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.590154230594635},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5498181581497192},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.5399278998374939},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5226473808288574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.506445050239563},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5035483241081238},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.469951868057251},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.44705525040626526},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.41086938977241516},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3552793264389038},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.06671872735023499},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3417188.3417191","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3417188.3417191","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2020 4th International Conference on Deep Learning Technologies (ICDLT)","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":8,"referenced_works":["https://openalex.org/W2014105136","https://openalex.org/W2032377318","https://openalex.org/W2097775060","https://openalex.org/W2098107196","https://openalex.org/W2128080104","https://openalex.org/W2556767548","https://openalex.org/W2621028221","https://openalex.org/W2992597875"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W4313052709","https://openalex.org/W2945274617","https://openalex.org/W2022929107","https://openalex.org/W2055202857","https://openalex.org/W1999008862","https://openalex.org/W4205800335","https://openalex.org/W2758994127"],"abstract_inverted_index":{"In":[0,33],"the":[1,7,13,23,61,70,99,102,107,121,126,138,145,151,160,165,184],"volume":[2,8,53,87,114,161],"image":[3,67,78,100,115,141],"of":[4,9,50,60,65,72,77,85,98,128,140],"brain":[5,51,112],"MRI,":[6],"hippocampus":[10,16,48,83],"is":[11,20,28,38,134,181],"small,":[12],"boundary":[14],"between":[15],"and":[17,22,45,74,80,101,116,132,150],"surrounding":[18],"tissue":[19],"fuzzy,":[21],"two-dimensional":[24],"semantic":[25],"segmentation":[26,49,84,122,162,186],"network":[27],"difficult":[29],"to":[30,92,136,158],"accurately":[31],"segment.":[32],"this":[34],"paper,":[35],"an":[36],"algorithm":[37],"proposed":[39],"which":[40,180],"combines":[41],"deep":[42],"residual":[43,130,152],"learning":[44,131,153],"U-net":[46,133],"for":[47],"MRI":[52,66,86,113],"image.":[54,88],"It":[55],"can":[56,177],"make":[57],"full":[58],"use":[59],"three-dimensional":[62],"spatial":[63],"information":[64,97],"itself,":[68],"improve":[69],"ability":[71],"automatic":[73],"precise":[75],"extraction":[76],"features,":[79],"achieve":[81],"high-precision":[82],"Firstly,":[89],"in":[90],"order":[91],"efficiently":[93],"utilize":[94],"3d":[95],"contextual":[96],"solve":[103],"class":[104],"imbalance":[105],"issue,":[106],"patches":[108],"were":[109],"extracted":[110],"from":[111],"put":[117],"into":[118],"network.":[119],"Then,":[120],"model":[123],"based":[124],"on":[125,168],"combination":[127],"depth":[129],"used":[135],"extract":[137],"features":[139],"patches.":[142],"After":[143],"that,":[144],"upper":[146],"sampling":[147],"feature":[148,154],"map":[149,155],"are":[156],"fused":[157],"get":[159],"results.":[163],"Finally,":[164],"detection":[166],"experiments":[167],"ADNI":[169],"dataset":[170],"show":[171],"that":[172],"DSC":[173],"(dice":[174],"similarity":[175],"coefficient)":[176],"reach":[178],"0.8915,":[179],"better":[182],"than":[183],"traditional":[185],"method.":[187]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}