{"id":"https://openalex.org/W7096093497","doi":null,"title":"Automatic Nuchal Translucency Measurement from","display_name":"Automatic Nuchal Translucency Measurement from","publication_year":2015,"publication_date":"2015-11-04","ids":{"openalex":"https://openalex.org/W7096093497"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.685.9113","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.685.9113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/%7Esofka/pdfs/park-miccai13.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jinhyeong Park","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinhyeong Park","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Michal Sofka","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michal Sofka","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sunmi Lee","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sunmi Lee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Daeyoung Kim","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daeyoung Kim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"S. Kevin Zhou","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S. Kevin Zhou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45938156,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965999722480774,"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":0.9965999722480774,"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/T10978","display_name":"Prenatal Screening and Diagnostics","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/T14510","display_name":"Medical Imaging and Analysis","score":0.0003000000142492354,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/nuchal-translucency","display_name":"nuchal translucency","score":0.9539999961853027},{"id":"https://openalex.org/keywords/automatic-measurement","display_name":"automated measurement","score":0.6579999923706055},{"id":"https://openalex.org/keywords/prenatal-ultrasound","display_name":"prenatal ultrasound","score":0.7289999723434448},{"id":"https://openalex.org/keywords/graph-cut-segmentation","display_name":"graph cut segmentation","score":0.9490000009536743},{"id":"https://openalex.org/keywords/mid-sagittal-plane","display_name":"mid-sagittal plane","score":0.7820000052452087},{"id":"https://openalex.org/keywords/prenatal-screening","display_name":"prenatal screening","score":0.44999998807907104}],"concepts":[{"id":"https://openalex.org/C2779811377","wikidata":"https://www.wikidata.org/wiki/Q5445900","display_name":"Fetal head","level":4,"score":0.7479000091552734},{"id":"https://openalex.org/C3017541802","wikidata":"https://www.wikidata.org/wiki/Q736944","display_name":"Nuchal translucency","level":5,"score":0.6385999917984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.625},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5665000081062317},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5353999733924866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48249998688697815},{"id":"https://openalex.org/C21353171","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Length measurement","level":2,"score":0.4260999858379364},{"id":"https://openalex.org/C2908689992","wikidata":"https://www.wikidata.org/wiki/Q2642447","display_name":"Biparietal diameter","level":5,"score":0.3564000129699707},{"id":"https://openalex.org/C2909999348","wikidata":"https://www.wikidata.org/wiki/Q736944","display_name":"Nuchal Translucency Measurement","level":5,"score":0.3549000024795532},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C3019831412","wikidata":"https://www.wikidata.org/wiki/Q5778278","display_name":"Fully automatic","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.2745000123977661},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2549000084400177},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.685.9113","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.685.9113","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.rpi.edu/%7Esofka/pdfs/park-miccai13.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.6240000128746033,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W46479022","https://openalex.org/W4402359007","https://openalex.org/W1531911649","https://openalex.org/W2756437557","https://openalex.org/W2525969955","https://openalex.org/W3216405755","https://openalex.org/W2142989868","https://openalex.org/W2409663929","https://openalex.org/W2576014699","https://openalex.org/W2122467551"],"abstract_inverted_index":{"Abstract.":[0],"This":[1,26],"paper":[2],"proposes":[3],"a":[4,23,72],"fully":[5],"automatic":[6],"approach":[7],"for":[8,134,168],"comput-ing":[9],"Nuchal":[10],"Translucency":[11],"(NT)":[12],"measurement":[13,33,41,141,164],"in":[14,45],"an":[15,28],"ultrasound":[16],"scans":[17],"of":[18,22,39,48,59,97],"the":[19,49,57,60,77,83,87,91,95,98,118,145,151,157],"mid-sagittal":[20],"plane":[21],"fetal":[24,61,68,88],"head.":[25],"is":[27,80,101,142],"improvement":[29],"upon":[30],"cur-rent":[31],"NT":[32,40,50,78,92,99,140,158,163],"methods":[34],"which":[35,165],"require":[36],"manual":[37],"placement":[38],"points":[42],"or":[43],"user-guidance":[44],"semi-automatic":[46],"segmentation":[47],"region.":[51,93,147],"The":[52,67,139],"algorithm":[53,152],"starts":[54],"by":[55],"finding":[56],"pose":[58,96],"head":[62,69,89],"using":[63],"discriminative":[64],"learning-based":[65],"detectors.":[66],"serves":[70],"as":[71],"robust":[73],"anchoring":[74],"structure":[75],"and":[76,90,104,107,131,154,160],"region":[79,100,124,159],"estimated":[81],"from":[82,144],"statistical":[84],"relationship":[85],"between":[86],"Next,":[94],"locally":[102],"refined":[103],"its":[105],"inner":[106],"outer":[108],"edge":[109],"approximately":[110],"determined":[111],"via":[112],"Dijkstra\u2019s":[113],"shortest":[114],"path":[115],"applied":[116],"on":[117],"edge-enhanced":[119],"image.":[120],"Finally,":[121],"these":[122],"two":[123],"edges":[125],"are":[126],"used":[127],"to":[128],"define":[129],"foreground":[130],"background":[132],"seeds":[133],"accurate":[135,162],"graph":[136],"cut":[137],"segmentation.":[138],"computed":[143],"segmented":[146],"Experi-ments":[148],"show":[149],"that":[150],"efficiently":[153],"effectively":[155],"detects":[156],"provides":[161],"suggests":[166],"suitability":[167],"clinical":[169],"use.":[170],"1":[171]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-27T00:00:00"}