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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.

Original publication

DOI

10.1016/j.ultrasmedbio.2017.07.013

Type

Journal article

Journal

Ultrasound Med Biol

Publication Date

12/2017

Volume

43

Pages

2925 - 2933

Keywords

Convolutional neural networks, Fetal ultrasound, Knowledge-based image analysis, Neurosonography, Pregnancy, Trans-ventricular plane