Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal.
H N XieNan WangM HeL H ZhangH M CaiJ B XianMei-Fang LinJ ZhengY Z YangPublished in: Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology (2021)
Deep-learning algorithms can be trained for segmentation and classification of normal and abnormal fetal brain ultrasound images in standard axial planes and can provide heat maps for lesion localization. This study lays the foundation for further research on the differential diagnosis of fetal intracranial abnormalities. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.
Keyphrases
- deep learning
- convolutional neural network
- machine learning
- artificial intelligence
- magnetic resonance imaging
- white matter
- resting state
- functional connectivity
- cerebral ischemia
- randomized controlled trial
- systematic review
- multiple sclerosis
- heat stress
- brain injury
- optical coherence tomography
- subarachnoid hemorrhage
- meta analyses