AIMC Topic: Magnetic Resonance Imaging

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Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage
OBJECTIVE: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof.

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

Scientific reports
To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals an...

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.

Psychiatry research. Neuroimaging
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) o...

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

Scientific reports
The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to pr...

Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images.

Medical image analysis
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is chal...

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Journal of neuroscience methods
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...

Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.

Journal of neuroscience methods
BACKGROUND: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algor...

Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning.

IEEE transactions on medical imaging
Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the throug...

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology ...

Shared and Subject-Specific Dictionary Learning (ShSSDL) Algorithm for Multisubject fMRI Data Analysis.

IEEE transactions on bio-medical engineering
OBJECTIVE: Analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects is at the heart of many medical imaging studies, and approaches based on dictionary learning (DL) are recently noted as promising solutions to the problem...