AIMC Topic: Brain

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Automated pixel-wise brain tissue segmentation of diffusion-weighted images via machine learning.

NMR in biomedicine
The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T relaxivity. This study aimed to implement a machine learning algorithm...

Single-shot T mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

Magnetic resonance in medicine
PURPOSE: An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T mapping from single-shot overlapping-echo detachment (OLED) planar imaging.

The inhibitory effect of functional lesions on eloquent brain areas: from research bench to operating bed.

The International journal of neuroscience
Functioning, but injured cerebral connections are hypothesized to inhibit cortical plasticity. Study of neural networks can validate this hypothesis, and provide further practical clues for clinical and surgical options to restore function in eloque...

3D Randomized Connection Network with Graph-based Label Inference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity. The convolutional LSTM and 3D convolution are ...

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as...

Super-resolution reconstruction of MR image with a novel residual learning network algorithm.

Physics in medicine and biology
Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural network...

Brain State Decoding Based on fMRI Using Semisupervised Sparse Representation Classifications.

Computational intelligence and neuroscience
Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to tr...

Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI.

International journal of neural systems
Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not h...

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning.

Epilepsia
OBJECTIVE: Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In ...

Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.

BMC psychiatry
BACKGROUND: Early diagnosis of schizophrenia could improve the outcome of the illness. Unlike classical between-group comparisons, machine learning can identify subtle disease patterns on a single subject level, which could help realize the potential...