A network neuroscience approach to typical and atypical brain development
Human brain networks based on neuroimaging data have already proven useful in
characterizing both normal and abnormal brain structure and function. However, many brain …
characterizing both normal and abnormal brain structure and function. However, many brain …
Multiscale brain MRI super-resolution using deep 3D convolutional networks
CH Pham, C Tor-Díez, H Meunier, N Bednarek… - … Medical Imaging and …, 2019 - Elsevier
The purpose of super-resolution approaches is to overcome the hardware limitations and
the clinical requirements of imaging procedures by reconstructing high-resolution images …
the clinical requirements of imaging procedures by reconstructing high-resolution images …
Real-time deep pose estimation with geodesic loss for image-to-template rigid registration
With an aim to increase the capture range and accelerate the performance of state-of-the-art
inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based …
inter-subject and subject-to-template 3-D rigid registration, we propose deep learning-based …
SegSRGAN: Super-resolution and segmentation using generative adversarial networks—Application to neonatal brain MRI
Background and objective: One of the main issues in the analysis of clinical neonatal brain
MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first …
MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first …
Quantitative assessment of the susceptibility artefact and its interaction with motion in diffusion MRI
In this paper we evaluate the three main methods for correcting the susceptibility-induced
artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how …
artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how …
Construction of a neonatal cortical surface atlas using multimodal surface matching in the developing human connectome project
We propose a method for constructing a spatio-temporal cortical surface atlas of neonatal
brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The …
brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The …
Using deep convolutional neural networks for neonatal brain image segmentation
Y Ding, R Acosta, V Enguix, S Suffren… - Frontiers in …, 2020 - frontiersin.org
Introduction Deep learning neural networks are especially potent at dealing with structured
data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed …
data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed …
Deep learning-based magnetic resonance image super-resolution: a survey
Magnetic resonance imaging (MRI) is a medical imaging technique used to show
anatomical structures and physiological processes of the human body. Due to limitations like …
anatomical structures and physiological processes of the human body. Due to limitations like …
Individual uniqueness in the neonatal functional connectome
The functional connectome is highly distinctive in adults and adolescents, underlying
individual differences in cognition and behavior. However, it remains unknown whether the …
individual differences in cognition and behavior. However, it remains unknown whether the …
[HTML][HTML] Autoencoding low-resolution MRI for semantically smooth interpolation of anisotropic MRI
High-resolution medical images are beneficial for analysis but their acquisition may not
always be feasible. Alternatively, high-resolution images can be created from low-resolution …
always be feasible. Alternatively, high-resolution images can be created from low-resolution …