AIMC Topic: Fetus
Clear Filters Showing 51 to 60 of 98 articles
Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.
Sensors (Basel, Switzerland)
Jun 30, 2021
Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more...
Editorial for "Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods".
Journal of magnetic resonance imaging : JMRI
Jun 1, 2021
Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.
Journal of magnetic resonance imaging : JMRI
Apr 23, 2021
BACKGROUND: Due to random motion of fetuses and maternal respirations, image quality of fetal brain MRIs varies considerably. To address this issue, visual inspection of the images is performed during acquisition phase and after 3D-reconstruction, an...
Fetal MRI-based artificial intelligence in gestational age prediction--a practical solution to an unsolved problem?
European radiology
Apr 22, 2021
Deep learning model for predicting gestational age after the first trimester using fetal MRI.
European radiology
Apr 14, 2021
OBJECTIVES: To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to biparietal diameter (BPD).
A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI.
IEEE transactions on medical imaging
Apr 1, 2021
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cortical plate, or manual refinement of automatic segmentations is tedious and time-consuming. Automati...
Recognition of facial expression of fetuses by artificial intelligence (AI).
Journal of perinatal medicine
Feb 8, 2021
OBJECTIVES: The development of the artificial intelligence (AI) classifier to recognize fetal facial expressions that are considered as being related to the brain development of fetuses as a retrospective, non-interventional pilot study.
Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.
IEEE transactions on medical imaging
Feb 2, 2021
Deep neural networks exhibit limited generalizability across images with different entangled domain features and categorical features. Learning generalizable features that can form universal categorical decision boundaries across domains is an intere...
Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Dec 29, 2020
Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we ...
Revealing architectural order with quantitative label-free imaging and deep learning.
eLife
Jul 27, 2020
We report quantitative label-free imaging with phase and polarization (QLIPP) for simultaneous measurement of density, anisotropy, and orientation of structures in unlabeled live cells and tissue slices. We combine QLIPP with deep neural networks to ...
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