AI Medical Compendium Topic:
Neuroimaging

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Deep learning guided stroke management: a review of clinical applications.

Journal of neurointerventional surgery
Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess...

Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging.

NeuroImage. Clinical
Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver va...

Deformable Image Registration based on Similarity-Steered CNN Regression.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deform...

An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement.

NeuroImage
This paper presents an open, multi-vendor, multi-field strength magnetic resonance (MR) T1-weighted volumetric brain imaging dataset, named Calgary-Campinas-359 (CC-359). The dataset is composed of images of older healthy adults (29-80 years) acquire...

Multi-modal discriminative dictionary learning for Alzheimer's disease and mild cognitive impairment.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The differentiation of mild cognitive impairment (MCI), which is the prodromal stage of Alzheimer's disease (AD), from normal control (NC) is important as the recent research emphasis on early pre-clinical stage for possible...

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Neuroscience and biobehavioral reviews
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder....

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

PloS one
Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy r...

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

NeuroImage. Clinical
Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-wei...

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.

Brain and behavior
INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task m...

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

NeuroImage
This study investigates a 3D and fully convolutional neural network (CNN) for subcortical brain structure segmentation in MRI. 3D CNN architectures have been generally avoided due to their computational and memory requirements during inference. We ad...