AIMC Topic: Magnetic Resonance Imaging

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Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks.

IEEE journal of biomedical and health informatics
In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modaliti...

The Ventral Visual Pathway Represents Animal Appearance over Animacy, Unlike Human Behavior and Deep Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Recent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual a...

Improving human cortical sulcal curve labeling in large scale cross-sectional MRI using deep neural networks.

Journal of neuroscience methods
BACKGROUND: Human cortical primary sulci are relatively stable landmarks and commonly observed across the population. Despite their stability, the primary sulci exhibit phenotypic variability.

MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.

Medical physics
PURPOSE: Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the pati...

Learning image-based spatial transformations via convolutional neural networks: A review.

Magnetic resonance imaging
Recent methodological innovations in deep learning and associated advancements in computational hardware have significantly impacted the various core subfields of quantitative medical image analysis. The generalizability, computational efficiency and...

A review on brain tumor segmentation of MRI images.

Magnetic resonance imaging
The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. This paper discusses a thorough literature review of recent methods of brain tumor segment...

Machine-learning based brain age estimation in major depression showing no evidence of accelerated aging.

Psychiatry research. Neuroimaging
Molecular biological findings indicate that affective disorders are associated with processes akin to accelerated aging of the brain. The use of the BrainAGE (brain age estimation gap) framework allows machine-learning based detection of a gap betwee...

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

Pairwise Interactions among Brain Regions Organize Large-Scale Functional Connectivity during Execution of Various Tasks.

Neuroscience
Spatially separated brain areas interact with each other to form networks with coordinated activities, supporting various brain functions. Interaction structures among brain areas have been widely investigated through pairwise measures. However, inte...

DRRNet: Dense Residual Refine Networks for Automatic Brain Tumor Segmentation.

Journal of medical systems
Glioma is one of the most common and aggressive brain tumors. Segmentation and subsequent quantitative analysis of brain tumor MRI are routine and crucial for treatment. Due to the time-consuming and tedious manual segmentation, automatic segmentatio...