{"id":"https://openalex.org/W3118140813","doi":"https://doi.org/10.1109/hpec43674.2020.9286243","title":"High-Throughput Image Alignment for Connectomics using Frugal Snap Judgments","display_name":"High-Throughput Image Alignment for Connectomics using Frugal Snap Judgments","publication_year":2020,"publication_date":"2020-09-22","ids":{"openalex":"https://openalex.org/W3118140813","doi":"https://doi.org/10.1109/hpec43674.2020.9286243","mag":"3118140813"},"language":"en","primary_location":{"id":"doi:10.1109/hpec43674.2020.9286243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec43674.2020.9286243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","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/A5003285620","display_name":"Tim Kaler","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tim Kaler","raw_affiliation_strings":["MIT CSAIL"],"affiliations":[{"raw_affiliation_string":"MIT CSAIL","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004423945","display_name":"Brian Wheatman","orcid":"https://orcid.org/0009-0005-2018-4577"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian Wheatman","raw_affiliation_strings":["Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069391235","display_name":"Sarah Wooders","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]},{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sarah Wooders","raw_affiliation_strings":["UC Berkeley"],"affiliations":[{"raw_affiliation_string":"UC Berkeley","institution_ids":["https://openalex.org/I134446601","https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003285620"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16081871,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1304 6034","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9934999942779541,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9934999942779541,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9682000279426575,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9660999774932861,"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/connectomics","display_name":"Connectomics","score":0.858025312423706},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7560898065567017},{"id":"https://openalex.org/keywords/snap","display_name":"Snap","score":0.6626302003860474},{"id":"https://openalex.org/keywords/throughput","display_name":"Throughput","score":0.6119634509086609},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.5701207518577576},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49360573291778564},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4462338089942932},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.2995986342430115},{"id":"https://openalex.org/keywords/connectome","display_name":"Connectome","score":0.20602014660835266},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.14599445462226868},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12690412998199463}],"concepts":[{"id":"https://openalex.org/C2779097318","wikidata":"https://www.wikidata.org/wiki/Q2993446","display_name":"Connectomics","level":4,"score":0.858025312423706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7560898065567017},{"id":"https://openalex.org/C2779997099","wikidata":"https://www.wikidata.org/wiki/Q7547253","display_name":"Snap","level":2,"score":0.6626302003860474},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.6119634509086609},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.5701207518577576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49360573291778564},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4462338089942932},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.2995986342430115},{"id":"https://openalex.org/C45715564","wikidata":"https://www.wikidata.org/wiki/Q1292103","display_name":"Connectome","level":3,"score":0.20602014660835266},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.14599445462226868},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12690412998199463},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.0},{"id":"https://openalex.org/C3018011982","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Functional connectivity","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpec43674.2020.9286243","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpec43674.2020.9286243","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","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":23,"referenced_works":["https://openalex.org/W1658644876","https://openalex.org/W1677409904","https://openalex.org/W1973824709","https://openalex.org/W2026354831","https://openalex.org/W2044593618","https://openalex.org/W2085261163","https://openalex.org/W2091782827","https://openalex.org/W2117228865","https://openalex.org/W2120403169","https://openalex.org/W2128366179","https://openalex.org/W2135063076","https://openalex.org/W2145327262","https://openalex.org/W2151103935","https://openalex.org/W2153185479","https://openalex.org/W2466361440","https://openalex.org/W2581356531","https://openalex.org/W2883672905","https://openalex.org/W2913772345","https://openalex.org/W3106525532","https://openalex.org/W3210232381","https://openalex.org/W4246166885","https://openalex.org/W6637400245","https://openalex.org/W6803376173"],"related_works":["https://openalex.org/W2263852508","https://openalex.org/W2424680827","https://openalex.org/W2212379672","https://openalex.org/W2807503652","https://openalex.org/W2532575085","https://openalex.org/W2079675757","https://openalex.org/W2912524409","https://openalex.org/W4226310282","https://openalex.org/W4251316676","https://openalex.org/W4323066529"],"abstract_inverted_index":{"The":[0,162,336,405],"accuracy":[1],"and":[2,33,41,52,57,120,142,145,172,178,190,207,215,226,231,294,316,319,346,352,364,385,388,415,421,433],"computational":[3],"efficiency":[4,114,288],"of":[5,12,22,82,102,124,169,199,256,276,298,343,373,412,442],"image":[6],"alignment":[7,43,94,164,217,268,338,407],"directly":[8],"affects":[9],"the":[10,20,23,30,89,122,140,194,204,263,296,314,368,383,437],"advancement":[11],"connectomics,":[13],"a":[14,64,68,73,100,167,184,197,238,242,247,274,341,358,371,410,427,440],"field":[15],"which":[16],"seeks":[17],"to":[18,38,67,128,159,212,241,302,333,402],"understand":[19],"structure":[21],"brain":[24],"through":[25],"electron":[26],"microscopy.":[27],"We":[28,202],"introduce":[29,203],"algorithms":[31,90,144,205,264,318,387],"Quilter":[32,51,141,206,225,315,384],"Stacker":[34,53,143,208,227,317,386],"that":[35,96,209,270],"are":[36,54,210,228],"designed":[37,211],"perform":[39,213],"2D":[40,214],"3D":[42,216],"respectively":[44,218],"on":[45,60,175,219,234,349,418],"petabyte-scale":[46,220],"data":[47,98,221,272],"sets":[48,222],"from":[49,63,110,116,121,156,223,237,284,290,295,330,399],"connectomics.":[50,224],"efficient,":[55,229],"scalable,":[56,230],"can":[58,232],"run":[59,233],"hardware":[61,235],"ranging":[62,236],"researcher's":[65,239],"laptop":[66,240],"large":[69,243],"computing":[70,244],"cluster.":[71,245],"On":[72,183,246,357,426],"single":[74,248],"18-core":[75,177,249,351,420],"cloud":[76,250],"machine":[77,251],"each":[78,252],"algorithm":[79,253],"achieves":[80,254],"throughputs":[81,255],"more":[83,257],"than":[84,258],"1":[85,259],"TB/hr;":[86,260],"when":[87,261],"combined":[88,262],"produce":[91,265],"an":[92,106,176,266,280,350,419],"end-to-end":[93,267],"pipeline":[95,136,165,195,269,310,339,369,379,408,438],"processes":[97,271],"at":[99,273],"rate":[101,275],"0.82":[103,277],"TB/hr":[104,171,174,278,345,348,414,417],"-":[105,279],"over":[107,281],"10x":[108,282],"improvement":[109,283],"previous":[111,285],"systems.":[112,286],"This":[113,287],"comes":[115,289],"both":[117,291],"traditional":[118,292],"optimizations":[119,293],"use":[123,297],"\u201cFrugal":[125,299],"Snap":[126,300],"Judgments\u201d":[127,301],"judiciously":[129,303],"exploit":[130,304],"performance-accuracy":[131,305],"trade-offs.":[132,306],"A":[133,307,376],"high-throughput":[134,308,377],"image-alignment":[135,309,378],"was":[137,148,311,322,380,391],"implemented":[138,312,381],"using":[139,150,313,324,382,393],"its":[146,320,389],"performance":[147,321,390],"evaluated":[149,323,392],"three":[151,325,394],"datasets":[152,326,395],"whose":[153,327,396],"size":[154,328,397],"ranged":[155,329,398],"550":[157,331,400],"GB":[158,332,401],"38":[160,334,403],"TB.":[161,335,404],"full":[163,337,406],"achieved":[166,196,340,370,409,439],"throughput":[168,198,342,372,411,441],"0.6-0.8":[170,344,413],"1.4-1.5":[173,347,416],"112-core":[179,353,422],"shared-memory":[180,354,423],"multicore,":[181,355,424],"respectively.":[182,356,425],"supercomputing":[185,359,428],"cluster":[186,360,429],"with":[187,361,430],"200":[188,362,431],"nodes":[189,363,432],"1600":[191,365,434],"total":[192,366,435],"cores,":[193,367,436],"21.4":[200,374,443],"TB/hr.":[201,375,444]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
