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Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images

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dc.contributor.author권자영-
dc.contributor.author박예진-
dc.date.accessioned2018-09-28T08:57:37Z-
dc.date.available2018-09-28T08:57:37Z-
dc.date.issued2018-
dc.identifier.issn2168-2194-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/163274-
dc.description.abstractUltrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors' decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.isPartOfIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleAutomatic Estimation of Fetal Abdominal Circumference From Ultrasound Images-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Obstetrics & Gynecology-
dc.contributor.googleauthorJaeseong Jang-
dc.contributor.googleauthorYejin Park-
dc.contributor.googleauthorBukweon Kim-
dc.contributor.googleauthorSung Min Lee-
dc.contributor.googleauthorJa-Young Kwon-
dc.contributor.googleauthorJin Keun Seo-
dc.identifier.doi10.1109/JBHI.2017.2776116-
dc.contributor.localIdA00246-
dc.contributor.localIdA04836-
dc.relation.journalcodeJ03267-
dc.identifier.eissn2168-2208-
dc.identifier.pmid29990257-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8116636-
dc.subject.keywordConvolutional neural network-
dc.subject.keywordfetal biometry-
dc.subject.keywordfetal ultrasound-
dc.contributor.alternativeNameKwon, Ja Young-
dc.contributor.alternativeNamePark, Yejin-
dc.contributor.affiliatedAuthorKwon, Ja Young-
dc.contributor.affiliatedAuthorPark, Yejin-
dc.citation.volume22-
dc.citation.number5-
dc.citation.startPage1512-
dc.citation.endPage1520-
dc.identifier.bibliographicCitationIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol.22(5) : 1512-1520, 2018-
dc.identifier.rimsid58539-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Obstetrics and Gynecology (산부인과학교실) > 1. Journal Papers

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