{"id":"https://openalex.org/W3195599729","doi":"https://doi.org/10.1109/icip42928.2021.9506174","title":"Multi-Scale Modeling of Neural Structure in X-Ray Imagery","display_name":"Multi-Scale Modeling of Neural Structure in X-Ray Imagery","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3195599729","doi":"https://doi.org/10.1109/icip42928.2021.9506174","mag":"3195599729"},"language":"en","primary_location":{"id":"doi:10.1109/icip42928.2021.9506174","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","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/A5014018371","display_name":"Aishwarya Balwani","orcid":"https://orcid.org/0000-0002-9234-1632"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aishwarya Balwani","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027591073","display_name":"Joseph Miano","orcid":"https://orcid.org/0000-0002-5193-1644"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph Miano","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020378699","display_name":"Ran Liu","orcid":"https://orcid.org/0000-0003-0007-3691"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ran Liu","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004820176","display_name":"Lindsey Kitchell","orcid":"https://orcid.org/0000-0002-5498-5306"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lindsey Kitchell","raw_affiliation_strings":["Johns Hopkins University Applied Physics Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University Applied Physics Laboratory","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001169085","display_name":"Judy A. Prasad","orcid":"https://orcid.org/0000-0001-6990-9333"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Judy A. Prasad","raw_affiliation_strings":["University of North Carolina at Chapel Hill"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Chapel Hill","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037437190","display_name":"Erik C. Johnson","orcid":"https://orcid.org/0000-0002-7397-8531"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erik C. Johnson","raw_affiliation_strings":["Johns Hopkins University Applied Physics Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University Applied Physics Laboratory","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067336192","display_name":"William Gray-Roncal","orcid":"https://orcid.org/0000-0002-7362-9665"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"William Gray-Roncal","raw_affiliation_strings":["Johns Hopkins University Applied Physics Laboratory"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University Applied Physics Laboratory","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064062853","display_name":"Eva L. Dyer","orcid":"https://orcid.org/0000-0002-6962-524X"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eva L. Dyer","raw_affiliation_strings":["Georgia Institute of Technology","Emory University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]},{"raw_affiliation_string":"Emory University","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"145"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9990000128746033,"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":0.9990000128746033,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8483966588973999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7426947951316833},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7315688133239746},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6708787083625793},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5725038647651672},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4961036145687103},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.49331632256507874},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46660539507865906},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4490967392921448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44473832845687866},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4137558937072754},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08046716451644897}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8483966588973999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7426947951316833},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7315688133239746},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6708787083625793},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5725038647651672},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4961036145687103},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.49331632256507874},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46660539507865906},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4490967392921448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44473832845687866},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4137558937072754},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08046716451644897},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip42928.2021.9506174","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506174","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1910109443","https://openalex.org/W1973824709","https://openalex.org/W1976032673","https://openalex.org/W2016277672","https://openalex.org/W2017478274","https://openalex.org/W2071366993","https://openalex.org/W2169118010","https://openalex.org/W2194775991","https://openalex.org/W2279851379","https://openalex.org/W2337567566","https://openalex.org/W2737485754","https://openalex.org/W2803790834","https://openalex.org/W2875369846","https://openalex.org/W2963881378","https://openalex.org/W2964150021","https://openalex.org/W2964152645","https://openalex.org/W2987188183","https://openalex.org/W2990601753","https://openalex.org/W3005680577","https://openalex.org/W3006657822","https://openalex.org/W3013416319","https://openalex.org/W3029273709","https://openalex.org/W3094284548","https://openalex.org/W3100262678","https://openalex.org/W4394096973","https://openalex.org/W4394233170","https://openalex.org/W6639824700","https://openalex.org/W6752246543","https://openalex.org/W6774314701"],"related_works":["https://openalex.org/W4206534706","https://openalex.org/W3006943036","https://openalex.org/W3191046242","https://openalex.org/W4299487748","https://openalex.org/W4206493799","https://openalex.org/W4213225422","https://openalex.org/W4294031299","https://openalex.org/W2605281151","https://openalex.org/W3208423683","https://openalex.org/W4213432687"],"abstract_inverted_index":{"Methods":[0],"for":[1,67,88],"resolving":[2],"the":[3,22,44,51,74,114,120,138],"brain\u2019s":[4],"microstructure":[5,46],"are":[6],"rapidly":[7],"improving,":[8],"allowing":[9],"us":[10],"to":[11,110,127],"image":[12],"large":[13],"brain":[14,29,54,91],"volumes":[15],"at":[16,61,93,158],"high":[17],"resolutions.":[18],"As":[19],"a":[20,62,84,98,103,128],"result,":[21],"interrogation":[23],"of":[24,43,50,90,119],"samples":[25,37],"spanning":[26,134],"multiple":[27,94,135],"diversified":[28],"regions":[30,136],"is":[31,152],"becoming":[32],"increasingly":[33],"common.":[34],"Understanding":[35],"these":[36],"often":[38,57],"requires":[39],"multi-scale":[40],"processing:":[41],"segmentation":[42,89],"detailed":[45],"and":[47,65,77,107,116,141,151],"large-scale":[48],"modelling":[49],"macrostructure.":[52],"Current":[53],"mapping":[55],"algorithms":[56],"analyze":[58],"data":[59],"only":[60],"single":[63],"scale,":[64],"optimization":[66],"each":[68],"scale":[69],"occurs":[70],"independently,":[71],"potentially":[72],"limiting":[73],"consistency,":[75],"performance,":[76],"interpretability.":[78],"In":[79],"this":[80],"work":[81],"we":[82],"introduce":[83],"deep":[85],"learning":[86,105],"framework":[87],"structure":[92],"scales.":[95,160],"We":[96,122],"leverage":[97],"modified":[99],"U-Net":[100],"architecture":[101,118],"with":[102,154],"multi-task":[104,149],"objective":[106],"unsupervised":[108],"pre-training":[109],"simultaneously":[111],"model":[112],"both":[113,159],"micro":[115],"macro":[117],"brain.":[121],"successfully":[123],"apply":[124],"our":[125,144],"methods":[126],"heterogeneous,":[129],"three-dimensional,":[130],"X-ray":[131],"micro-CT":[132],"dataset":[133],"in":[137],"mouse":[139],"brain,":[140],"show":[142],"that":[143],"approach":[145],"consistently":[146],"outperforms":[147],"another":[148],"architecture,":[150],"competitive":[153],"strong":[155],"single-task":[156],"baselines":[157]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}