Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals …
This work presents a novel approach to finding linkage/association between multimodal
brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by …
brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by …
A Review on Alzheimer's disease through analysis of MRI images using Deep Learning Techniques
BS Rao, M Aparna - IEEE Access, 2023 - ieeexplore.ieee.org
The anatomical structure of the brain has been studied with the help of magnetic resonance
imaging (MRI), which has been used to analyze numerous neurological diseases and define …
imaging (MRI), which has been used to analyze numerous neurological diseases and define …
Head mouse control system for people with disabilities
In this paper, a human–machine interface for disabled people with spinal cord injuries is
proposed. The designed human–machine interface is an assistive system that uses head …
proposed. The designed human–machine interface is an assistive system that uses head …
vol2Brain: a new online pipeline for whole brain MRI analysis
Automatic and reliable quantitative tools for MR brain image analysis are a very valuable
resource for both clinical and research environments. In the past few years, this field has …
resource for both clinical and research environments. In the past few years, this field has …
Multi-center brain imaging classification using a novel 3D CNN approach
L Yuan, X Wei, H Shen, LL Zeng, D Hu - IEEE Access, 2018 - ieeexplore.ieee.org
With the development of brain imaging technology, increasing amounts of magnetic
resonance imaging data are being acquired, and traditional computational analysis methods …
resonance imaging data are being acquired, and traditional computational analysis methods …
Hippocampal segmentation in brain mri images using machine learning methods: A survey
The hippocampus is closely related to many brain diseases, such as Alzheimer's disease.
Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and …
Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and …
Convolutional neural networks enable robust automatic segmentation of the rat hippocampus in mri after traumatic brain injury
R De Feo, E Hämäläinen, E Manninen… - Frontiers in …, 2022 - frontiersin.org
Registration-based methods are commonly used in the automatic segmentation of magnetic
resonance (MR) brain images. However, these methods are not robust to the presence of …
resonance (MR) brain images. However, these methods are not robust to the presence of …
Multi-view semi-supervised 3D whole brain segmentation with a self-ensemble network
Despite remarkable progress, 3D whole brain segmentation of structural magnetic
resonance imaging (MRI) into a large number of regions (> 100) is still difficult due to the …
resonance imaging (MRI) into a large number of regions (> 100) is still difficult due to the …
Machine learning and deep learning in medicine and neuroimaging
I Sánchez Fernández, JM Peters - Annals of the Child …, 2023 - Wiley Online Library
Artificial intelligence is the science and engineering of machines that can mimic human
intelligence. Machine learning is the subfield of artificial intelligence in which computers …
intelligence. Machine learning is the subfield of artificial intelligence in which computers …
CAN: Context-assisted full Attention Network for brain tissue segmentation
Z Li, C Zhang, Y Zhang, X Wang, X Ma, H Zhang… - Medical Image …, 2023 - Elsevier
Brain tissue segmentation is of great value in diagnosing brain disorders. Three-
dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic …
dimensional (3D) and two-dimensional (2D) segmentation methods for brain Magnetic …