AI Medical Compendium Topic:
Neuroimaging

Clear Filters Showing 481 to 490 of 807 articles

Morphological Neuroimaging Biomarkers for Tinnitus: Evidence Obtained by Applying Machine Learning.

Neural plasticity
According to previous studies, many neuroanatomical alterations have been detected in patients with tinnitus. However, the results of these studies have been inconsistent. The objective of this study was to explore the cortical/subcortical morphologi...

Deep learning segmentation of orbital fat to calibrate conventional MRI for longitudinal studies.

NeuroImage
In conventional non-quantitative magnetic resonance imaging, image contrast is consistent within images, but absolute intensity can vary arbitrarily between scans. For quantitative analysis of intensity data, images are typically normalized to a cons...

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.

NeuroImage. Clinical
The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of 'new or enlarged' is not fixed, and it is known that lesion-...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

NeuroImage. Clinical
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by...

Discriminative margin-sensitive autoencoder for collective multi-view disease analysis.

Neural networks : the official journal of the International Neural Network Society
Medical prediction is always collectively determined based on bioimages collected from different sources or various clinical characterizations described from multiple physiological features. Notably, learning intrinsic structures from multiple hetero...

Disentangling brain functional network remodeling in corticobasal syndrome - A multimodal MRI study.

NeuroImage. Clinical
OBJECTIVE: The clinical diagnosis of corticobasal syndrome (CBS) represents a challenge for physicians and reliable diagnostic imaging biomarkers would support the diagnostic work-up. We aimed to investigate the neural signatures of CBS using multimo...

A novel hybrid atlas-free hierarchical graph-based segmentation of newborn brain MRI using wavelet filter banks.

The International journal of neuroscience
: The newborn brain MRI (magnetic resonance imaging) tissue segmentation plays a vital part in assessment of primary brain growth. In the newborn stage (nearly less than 28 days old), in T1- as well as T2-weighted MR images similar levels of intensit...

Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Computer methods and programs in biomedicine
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. I...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Human brain mapping
Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the d...

Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage.

Stroke
Background and Purpose- Hematoma volume measurements influence prognosis and treatment decisions in patients with spontaneous intracerebral hemorrhage (ICH). The aims of this study are to derive and validate a fully automated segmentation algorithm f...