AIMC Topic: Magnetic Resonance Imaging

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Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder.

PLoS computational biology
While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understoo...

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network.

Magnetic resonance in medicine
PURPOSE: Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T -weighted MRI to calculate TKV of healthy control (HC) and chronic kidney diseas...

MRzero - Automated discovery of MRI sequences using supervised learning.

Magnetic resonance in medicine
PURPOSE: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient ex...

Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging.

Scientific reports
The goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previous...

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived ...

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers.

Sensors (Basel, Switzerland)
Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed fr...

Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear.

Sensors (Basel, Switzerland)
A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Al...

Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiac-function evaluation and diagnosis. In order to have a com...

Brain tissues have single-voxel signatures in multi-spectral MRI.

NeuroImage
Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based...

Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Human brain mapping
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their...