AIMC Topic: Magnetic Resonance Imaging
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A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.
NeuroImage
May 12, 2022
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disord...
Uncovering shape signatures of resting-state functional connectivity by geometric deep learning on Riemannian manifold.
Human brain mapping
May 10, 2022
Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for function...
The Curative Effect of Pregabalin in the Treatment of Postherpetic Neuralgia Analyzed by Deep Learning-Based Brain Resting-State Functional Magnetic Resonance Images.
Contrast media & molecular imaging
May 10, 2022
This work aimed to investigate the brain resting-state functional magnetic resonance imaging (fMRI) technology based on the depth autoencoders algorithm and to evaluate the clinically curative effect of pregabalin in the treatment of postherpetic neu...
Deep learning kidney segmentation with very limited training data using a cascaded convolution neural network.
PloS one
May 9, 2022
BACKGROUND: Deep learning segmentation requires large datasets with ground truth. Image annotation is time consuming and leads to shortages of ground truth data for clinical imaging. This study is to investigate the feasibility of kidney segmentation...
Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.
BMC musculoskeletal disorders
May 6, 2022
BACKGROUND: Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This stu...
Editorial for "Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning".
Journal of magnetic resonance imaging : JMRI
May 5, 2022
A Novel Framework With Weighted Decision Map Based on Convolutional Neural Network for Cardiac MR Segmentation.
IEEE journal of biomedical and health informatics
May 5, 2022
For diagnosing cardiovascular disease, an accurate segmentation method is needed. There are several unresolved issues in the complex field of cardiac magnetic resonance imaging, some of which have been partially addressed by using deep neural network...
AutoAtlas: Neural Network for 3D Unsupervised Partitioning and Representation Learning.
IEEE journal of biomedical and health informatics
May 5, 2022
We present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural netw...
Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning.
Journal of magnetic resonance imaging : JMRI
May 4, 2022
BACKGROUND: Segmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.
Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms.
Medicina (Kaunas, Lithuania)
May 4, 2022
: Malignant bone tumors represent a major problem due to their aggressiveness and low survival rate. One of the determining factors for improving vital and functional prognosis is the shortening of the time between the onset of symptoms and the momen...
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