{"id":"https://openalex.org/W6888442091","doi":"https://doi.org/10.21227/33h3-qx42","title":"Ultrasound spinal cord dataset","display_name":"Ultrasound spinal cord dataset","publication_year":2024,"publication_date":"2024-06-28","ids":{"openalex":"https://openalex.org/W6888442091","doi":"https://doi.org/10.21227/33h3-qx42"},"language":"en","primary_location":{"id":"doi:10.21227/33h3-qx42","is_oa":true,"landing_page_url":"https://doi.org/10.21227/33h3-qx42","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/33h3-qx42","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kumar, Avisha","orcid":null},"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":true,"raw_author_name":"Kumar, Avisha","raw_affiliation_strings":["Johns Hopkins University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7666000127792358},{"id":"https://openalex.org/keywords/spinal-cord","display_name":"Spinal cord","score":0.4404999911785126},{"id":"https://openalex.org/keywords/sagittal-plane","display_name":"Sagittal plane","score":0.42559999227523804},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42340001463890076},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.42179998755455017},{"id":"https://openalex.org/keywords/3d-ultrasound","display_name":"3D ultrasound","score":0.41609999537467957},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.40230000019073486},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.37610000371932983}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7666000127792358},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6031000018119812},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.48260000348091125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47200000286102295},{"id":"https://openalex.org/C2780775167","wikidata":"https://www.wikidata.org/wiki/Q9606","display_name":"Spinal cord","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C178910020","wikidata":"https://www.wikidata.org/wiki/Q2211994","display_name":"Sagittal plane","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42340001463890076},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C2780170424","wikidata":"https://www.wikidata.org/wiki/Q229399","display_name":"3D ultrasound","level":3,"score":0.41609999537467957},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.40230000019073486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3483000099658966},{"id":"https://openalex.org/C2778334475","wikidata":"https://www.wikidata.org/wiki/Q1415275","display_name":"Spinal cord injury","level":3,"score":0.3310999870300293},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.275299996137619},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C2986892559","wikidata":"https://www.wikidata.org/wiki/Q234904","display_name":"Ultrasound imaging","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/33h3-qx42","is_oa":true,"landing_page_url":"https://doi.org/10.21227/33h3-qx42","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.21227/33h3-qx42","is_oa":true,"landing_page_url":"https://doi.org/10.21227/33h3-qx42","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"deep":[1],"learning":[2],"has":[3],"catalyzed":[4],"breakthroughs":[5],"across":[6],"numerous":[7],"domains,":[8],"its":[9],"broader":[10],"adoption":[11],"in":[12,226],"clinical":[13,234],"settings":[14],"is":[15,117,191],"inhibited":[16],"by":[17],"the":[18,62,73,83,95,100,128,152,157,174,185,192,212,227],"costly":[19],"and":[20,26,54,77,87,206,219,233],"time-intensive":[21],"nature":[22],"of":[23,39,45,48,65,75,89,99,146,169,187,196,216],"data":[24],"acquisition":[25],"annotation.":[27],"To":[28,184],"further":[29],"facilitate":[30],"medical":[31,207],"machine":[32],"learning,":[33],"we":[34,93],"present":[35],"an":[36],"ultrasound":[37,105,199],"dataset":[38,116,195],"10,223":[40],"Brightness-mode":[41],"(B-mode)":[42],"images":[43,108,200],"consisting":[44],"sagittal":[46],"slices":[47],"porcine":[49,115,162],"spinal":[50,106,197,228],"cords":[51],"(N=25)":[52],"before":[53],"after":[55],"a":[56,140,165],"contusion":[57],"injury.":[58],"We":[59],"additionally":[60],"benchmark":[61],"performance":[63],"metrics":[64,149],"several":[66],"state-of-the-art":[67],"object":[68,217],"detection":[69,130,218],"algorithms":[70],"to":[71,81,109,180,204,222],"localize":[72],"site":[74],"injury":[76,137],"semantic":[78],"segmentation":[79,101,154,220],"models":[80,102,135],"label":[82],"anatomy":[84,182],"for":[85,119,136,230],"comparison":[86],"creation":[88],"task-specific":[90],"architectures.":[91],"Finally,":[92],"evaluate":[94],"zero-shot":[96],"generalization":[97],"capabilities":[98],"on":[103,113,160],"human":[104,122,181],"cord":[107,198,229],"determine":[110],"whether":[111],"training":[112],"our":[114,188],"sufficient":[118],"accurately":[120],"interpreting":[121],"data.":[123],"Our":[124],"results":[125],"show":[126],"that":[127,151],"YOLOv8":[129],"model":[131,155],"outperforms":[132],"all":[133],"evaluated":[134],"localization,":[138],"achieving":[139],"mean":[141],"Average":[142],"Precision":[143],"(mAP50-95)":[144],"score":[145,168,178],"0.606.":[147],"Segmentation":[148],"indicate":[150],"DeepLabv3":[153],"achieves":[156,173],"highest":[158,175],"accuracy":[159],"unseen":[161],"anatomy,":[163],"with":[164],"Mean":[166,176],"Dice":[167,177],"0.587,":[170],"while":[171],"SAMed":[172],"generalizing":[179],"(0.445).":[183],"best":[186],"knowledge,":[189],"this":[190],"largest":[193],"annotated":[194],"made":[201],"publicly":[202],"available":[203],"researchers":[205],"professionals,":[208],"as":[209,211],"well":[210],"first":[213],"public":[214],"report":[215],"architectures":[221],"assess":[223],"anatomical":[224],"markers":[225],"methodology":[231],"development":[232],"applications.&nbsp;&nbsp;":[235]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
