A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints.
During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessmen...
| Publicado en: | Ultrasound in Medicine & Biology Vol. 43; no. 12; pp. 2925 - 2934 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Dec2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126210853&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126210853 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03015629 JJ6 jtl: Ultrasound in Medicine & Biology issn: 03015629 maglogo: N pubinfo: dt: Dec2017 vid: 43 iid: 12 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 126210853 126210853 NLM28958729 126210853 10.1016/j.ultrasmedbio.2017.07.013 NLM28958729 126210853 ppf: 2925 ppct: 9 formats: tig: atl: A Deep Learning Solution for Automatic Fetal Neurosonographic Diagnostic Plane Verification Using Clinical Standard Constraints. aug: au: Yaqub, Mohammad Kelly, Brenda Papageorghiou, Aris T. Noble, J. Alison affil: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK sug: subj: Brain Embryology Image Processing, Computer Assisted Methods Brain Ultrasonography, Prenatal Methods Neural Networks (Computer) Female Pregnancy Human Female ab: During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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