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Gray Matter

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DeepQSMSeg: A Deep Learning-based Sub-cortical Nucleus Segmentation Tool for Quantitative Susceptibility Mapping.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep brain nuclei are closely related to the pathogenesis of neurodegenerative diseases. Automatic segmentation for brain nuclei plays a significant role in aging and disease-related assessment. Quantitative susceptibility mapping (QSM), as a novel M...

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation.

Computers in biology and medicine
Accurate measurement of brain structures is essential for the evaluation of neonatal brain growth and development. The conventional methods use manual segmentation to measure brain tissues, which is very time-consuming and inefficient. Recent deep le...

Probing the link between the APOE-ε4 allele and whole-brain gray matter using deep learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The APOE-ε4 allele is a known genetic risk for Alzheimer's disease (AD). Thus, it can be reasoned that the APOE-ε4 allele would also impact neurodegeneration-associated structural brain changes. Here we probe if the APOE-ε4 genotype directly modulate...

Voxel-based morphometry and a deep learning model for the diagnosis of early Alzheimer's disease based on cerebral gray matter changes.

Cerebral cortex (New York, N.Y. : 1991)
This study aimed to analyse cerebral grey matter changes in mild cognitive impairment (MCI) using voxel-based morphometry and to diagnose early Alzheimer's disease using deep learning methods based on convolutional neural networks (CNNs) evaluating t...

HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation.

European journal of radiology
A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzh...

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Human brain mapping
Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution ...

verified anatomically aware deep learning for real-time electric field simulation.

Journal of neural engineering
Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it en...

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonance imaging. Detailed morphological changes in a specific tissue area having the most evidence of atro...

Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine le...

Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach.

Medical physics
BACKGROUND: Cushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). W...