{"id":"https://openalex.org/W3092226997","doi":"https://doi.org/10.1109/tvcg.2020.3030374","title":"VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data","display_name":"VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data","publication_year":2020,"publication_date":"2020-10-13","ids":{"openalex":"https://openalex.org/W3092226997","doi":"https://doi.org/10.1109/tvcg.2020.3030374","mag":"3092226997","pmid":"https://pubmed.ncbi.nlm.nih.gov/33048701"},"language":"en","primary_location":{"id":"doi:10.1109/tvcg.2020.3030374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvcg.2020.3030374","pdf_url":null,"source":{"id":"https://openalex.org/S84775595","display_name":"IEEE Transactions on Visualization and Computer Graphics","issn_l":"1077-2626","issn":["1077-2626","1941-0506","2160-9306"],"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 Transactions on Visualization and Computer Graphics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5100398611","display_name":"Yifan Wang","orcid":"https://orcid.org/0009-0006-2759-2730"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yifan Wang","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004534638","display_name":"Guoli Yan","orcid":"https://orcid.org/0000-0002-2501-5300"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guoli Yan","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000705218","display_name":"Haikuan Zhu","orcid":"https://orcid.org/0000-0001-5740-0599"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haikuan Zhu","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029379745","display_name":"Sagar Buch","orcid":"https://orcid.org/0000-0001-9483-269X"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sagar Buch","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100346965","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0001-5172-4736"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049451858","display_name":"E. Mark Haacke","orcid":"https://orcid.org/0000-0003-4965-7954"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ewart Mark Haacke","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102022483","display_name":"Jing Hua","orcid":"https://orcid.org/0000-0002-3981-2933"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Hua","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082598478","display_name":"Zichun Zhong","orcid":"https://orcid.org/0000-0001-6489-6502"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zichun Zhong","raw_affiliation_strings":["Wayne State University, Detroit, MI"],"affiliations":[{"raw_affiliation_string":"Wayne State University, Detroit, MI","institution_ids":["https://openalex.org/I185443292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5100398611"],"corresponding_institution_ids":["https://openalex.org/I185443292"],"apc_list":null,"apc_paid":null,"fwci":2.5553,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.91449752,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"27","issue":"2","first_page":"1301","last_page":"1311"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9979000091552734,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9979000091552734,"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/T10816","display_name":"Cerebrovascular and Carotid Artery Diseases","score":0.9955999851226807,"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.994700014591217,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.771568775177002},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6450698971748352},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6312316656112671},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5311839580535889},{"id":"https://openalex.org/keywords/volume-rendering","display_name":"Volume rendering","score":0.5271772146224976},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5156711339950562},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4863298535346985},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4841388165950775},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46572229266166687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.414273202419281}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.771568775177002},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6450698971748352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6312316656112671},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5311839580535889},{"id":"https://openalex.org/C30769735","wikidata":"https://www.wikidata.org/wiki/Q2165951","display_name":"Volume rendering","level":3,"score":0.5271772146224976},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5156711339950562},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4863298535346985},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4841388165950775},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46572229266166687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.414273202419281},{"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}],"mesh":[{"descriptor_ui":"D003196","descriptor_name":"Computer Graphics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003196","descriptor_name":"Computer Graphics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003196","descriptor_name":"Computer Graphics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D021621","descriptor_name":"Imaging, Three-Dimensional","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/tvcg.2020.3030374","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvcg.2020.3030374","pdf_url":null,"source":{"id":"https://openalex.org/S84775595","display_name":"IEEE Transactions on Visualization and Computer Graphics","issn_l":"1077-2626","issn":["1077-2626","1941-0506","2160-9306"],"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 Transactions on Visualization and Computer Graphics","raw_type":"journal-article"},{"id":"pmid:33048701","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33048701","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on visualization and computer graphics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5281250125","display_name":null,"funder_award_id":"61972353","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W14869171","https://openalex.org/W1526189567","https://openalex.org/W1579287100","https://openalex.org/W1593855753","https://openalex.org/W1705281659","https://openalex.org/W1901129140","https://openalex.org/W1998964384","https://openalex.org/W2006594119","https://openalex.org/W2072579321","https://openalex.org/W2099275521","https://openalex.org/W2104355613","https://openalex.org/W2104424443","https://openalex.org/W2115900472","https://openalex.org/W2124620706","https://openalex.org/W2129534965","https://openalex.org/W2142777766","https://openalex.org/W2146336531","https://openalex.org/W2150312009","https://openalex.org/W2163278458","https://openalex.org/W2206167351","https://openalex.org/W2274287116","https://openalex.org/W2327793514","https://openalex.org/W2464708700","https://openalex.org/W2527341761","https://openalex.org/W2558748708","https://openalex.org/W2560609797","https://openalex.org/W2560722161","https://openalex.org/W2620722267","https://openalex.org/W2769549580","https://openalex.org/W2780485466","https://openalex.org/W2794642825","https://openalex.org/W2794803201","https://openalex.org/W2902149960","https://openalex.org/W2903073752","https://openalex.org/W2947448622","https://openalex.org/W2962701877","https://openalex.org/W2962778872","https://openalex.org/W2963021451","https://openalex.org/W2964350391","https://openalex.org/W2969885416","https://openalex.org/W2971614929","https://openalex.org/W2996421267","https://openalex.org/W2997284989","https://openalex.org/W3005757570","https://openalex.org/W3039752613","https://openalex.org/W3160693705","https://openalex.org/W4300956932","https://openalex.org/W6639824700","https://openalex.org/W6694260854","https://openalex.org/W6718752587","https://openalex.org/W6750104346","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W2081900870","https://openalex.org/W2068608913","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W2739976646","https://openalex.org/W1982370580","https://openalex.org/W1486114612"],"abstract_inverted_index":{"The":[0,126,159,326],"fundamental":[1],"motivation":[2],"of":[3,62,99,182,186,328,334,356],"the":[4,45,72,105,115,123,133,150,155,165,172,177,204,226,231,240,246,251,259,277,303,310,346],"proposed":[5,200,241,268],"work":[6],"is":[7,66,129,185,199,237,258,267],"to":[8,15,44,70,121,130,148,176,201,269],"present":[9,89],"a":[10,38,140,219,263,271,349],"new":[11],"visualization-guided":[12],"computing":[13],"paradigm":[14],"combine":[16],"direct":[17],"3D":[18,27,74,81,100,116,145,151,205,232,288,305,338],"volume":[19,22,134,141,146,206,233,282,289],"processing":[20],"and":[21,32,48,59,77,174,180,207,213,249,287,293,300,309,319,332,336,353],"rendered":[23],"clues":[24],"for":[25,96,144],"effective":[26],"exploration.":[28],"For":[29],"example,":[30],"extracting":[31],"visualizing":[33],"microstructures":[34],"in-vivo":[35],"have":[36],"been":[37],"long-standing":[39],"challenging":[40,69],"problem.":[41],"However,":[42],"due":[43],"high":[46,83],"sparseness":[47],"noisiness":[49],"in":[50,80,189,218,312,348],"cerebrovasculature":[51],"data":[52,152],"as":[53,55],"well":[54],"highly":[56],"complex":[57],"geometry":[58],"topology":[60],"variations":[61],"micro":[63],"vessels,":[64,183],"it":[65,79],"still":[67],"extremely":[68],"extract":[71],"complete":[73],"vessel":[75,167,252,279,306],"structure":[76,102,340],"visualize":[78],"with":[82,302],"fidelity.":[84],"In":[85],"this":[86,257,329],"paper,":[87],"we":[88],"an":[90],"end-to-end":[91],"deep":[92,156,264,313],"learning":[93,119,157,265],"method,":[94],"VC-Net,":[95],"robust":[97],"extraction":[98],"microvascular":[101,190,339],"through":[103],"embedding":[104,161,222,234,274],"image":[106,118,324],"composition,":[107],"generated":[108],"by":[109,224,315,342],"maximum":[110],"intensity":[111],"projection":[112,286],"(MIP),":[113],"into":[114,230],"volumetric":[117],"process":[120],"enhance":[122,149,164],"overall":[124],"performance.":[125],"core":[127],"novelty":[128],"automatically":[131],"leverage":[132],"visualization":[135,333],"technique":[136],"(e.g.,":[137],"MIP":[138,160,209],"-":[139],"rendering":[142,283],"scheme":[143],"images)":[147],"exploration":[153],"at":[154],"level.":[158],"features":[162],"can":[163,243,290],"local":[166],"signal":[168],"(through":[169],"canceling":[170],"out":[171],"noise)":[173],"adapt":[175],"geometric":[178],"variability":[179],"scalability":[181],"which":[184],"great":[187],"importance":[188],"tracking.":[191],"A":[192],"multi-stream":[193],"convolutional":[194,273],"neural":[195],"network":[196],"(CNN)":[197],"framework":[198,242,266],"effectively":[202],"learn":[203],"2D":[208,227,285],"feature":[210,228],"vectors,":[211],"respectively,":[212],"then":[214],"explore":[215],"their":[216],"inter-dependencies":[217],"joint":[220,272],"volume-composition":[221],"space":[223],"unprojecting":[225],"vectors":[229],"space.":[235],"It":[236],"noted":[238],"that":[239,262],"better":[244],"capture":[245],"small/micro":[247],"vessels":[248],"improve":[250],"connectivity.":[253],"To":[254],"our":[255,343],"knowledge,":[256],"first":[260],"time":[261],"construct":[270],"space,":[275],"where":[276],"computed":[278],"probabilities":[280],"from":[281],"based":[284],"be":[291],"explored":[292],"integrated":[294],"synergistically.":[295],"Experimental":[296],"results":[297],"are":[298],"evaluated":[299],"compared":[301],"traditional":[304],"segmentation":[307,331],"methods":[308],"state-of-the-art":[311],"learning,":[314],"using":[316],"extensive":[317],"public":[318],"real":[320],"patient":[321],"(micro-":[322],")cerebrovascular":[323],"datasets.":[325],"application":[327],"accurate":[330],"sparse":[335],"complicated":[337],"facilitated":[341],"method":[344],"demonstrates":[345],"potential":[347],"powerful":[350],"MR":[351],"arteriogram":[352],"venogram":[354],"diagnosis":[355],"vascular":[357],"disease.":[358]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":5}],"updated_date":"2026-03-12T08:34:05.389933","created_date":"2025-10-10T00:00:00"}
