0 46

Cited 0 times in

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images

DC FieldValueLanguage
dc.contributor.author권자영-
dc.contributor.author박예진-
dc.date.accessioned2019-01-15T17:06:17Z-
dc.date.available2019-01-15T17:06:17Z-
dc.date.issued2018-
dc.identifier.issn0967-3334-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/166808-
dc.description.abstractOBJECTIVE: Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. APPROACH: This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process. These machine learning techniques take clinicians' decisions, anatomical structures, and the characteristics of ultrasound images into account. The proposed method is divided into three steps: initial abdominal circumference (AC) estimation, AC measurement, and plane acceptance checking. MAIN RESULTS: A CNN is used to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein), and a Hough transform is used to obtain an initial estimate of the AC. These data are applied to other CNNs to estimate the spine position and bone regions. Then, the obtained information is used to determine the final AC. After determining the AC, a U-Net and a classification CNN are used to check whether the image is suitable for AC measurement. Finally, the efficacy of the proposed method is validated by clinical data. SIGNIFICANCE: Our method achieved a Dice similarity metric of [Formula: see text] for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherIOP Pub. Ltd.-
dc.relation.isPartOfPhysiological Measurement-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleMachine-learning-based automatic identification of fetal abdominal circumference from ultrasound images-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Obstetrics and Gynecology (산부인과학교실)-
dc.contributor.googleauthorBukweon Kim-
dc.contributor.googleauthorKang Cheol Kim-
dc.contributor.googleauthorYejin Park-
dc.contributor.googleauthorJa-Young Kwon-
dc.contributor.googleauthorJaeseong Jang-
dc.contributor.googleauthorJin Keun Seo-
dc.identifier.doi10.1088/1361-6579/aae255-
dc.contributor.localIdA00246-
dc.contributor.localIdA04836-
dc.contributor.localIdA04836-
dc.relation.journalcodeJ02527-
dc.identifier.eissn1361-6579-
dc.identifier.pmid30226815-
dc.identifier.urlhttp://iopscience.iop.org/article/10.1088/1361-6579/aae255/meta-
dc.contributor.alternativeNameKwon, Ja Young-
dc.contributor.affiliatedAuthor권자영-
dc.contributor.affiliatedAuthor박예진-
dc.contributor.affiliatedAuthor박예진-
dc.citation.volume39-
dc.citation.number10-
dc.citation.startPage105007-
dc.identifier.bibliographicCitationPhysiological Measurement, Vol.39(10) : 105007, 2018-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Obstetrics and Gynecology (산부인과학교실) > 1. Journal Papers

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.