AIMC Topic: Magnetic Resonance Imaging
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Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces.
Scientific reports
Aug 18, 2021
A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key ...
Automatic measurement plane placement for 4D Flow MRI of the great vessels using deep learning.
International journal of computer assisted radiology and surgery
Aug 17, 2021
PURPOSE: Despite the great potential and flexibility of 4D flow MRI for hemodynamic analysis, a major limitation is the need for time-consuming and user-dependent post-processing. We propose a fast four-step algorithm for rapid, robust, and repeatabl...
Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Aug 16, 2021
OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to accurately classify Healthy Controls, Early Mild Cognitive Impairment, Late Mild Cognitive Impairment,...
Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions.
Scientific reports
Aug 16, 2021
Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The quantification of MFI requires time-consuming and rater-dependent manual segmentation techniques. A convolutional neural network (CNN) model was trained to se...
TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 14, 2021
Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease diagnosis. It is still an open problem due to some difficulties, such as a large variety of object s...
Feature-fusion improves MRI single-shot deep learning detection of small brain metastases.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
Aug 13, 2021
BACKGROUND AND PURPOSE: To examine whether feature-fusion (FF) method improves single-shot detector's (SSD's) detection of small brain metastases on contrast-enhanced (CE) T1-weighted MRI.
Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.
Human brain mapping
Aug 13, 2021
Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing thre...
A novel M-SegNet with global attention CNN architecture for automatic segmentation of brain MRI.
Computers in biology and medicine
Aug 13, 2021
In this paper, we propose a novel M-SegNet architecture with global attention for the segmentation of brain magnetic resonance imaging (MRI). The proposed architecture consists of a multiscale deep network at the encoder side, deep supervision at the...
Unveiling functions of the visual cortex using task-specific deep neural networks.
PLoS computational biology
Aug 13, 2021
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We r...
Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis.
Psychophysiology
Aug 12, 2021
Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to iden...
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