QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a
prerequisite for most morphological analyses, but is computationally intense and can …
prerequisite for most morphological analyses, but is computationally intense and can …
DeepNAT: Deep convolutional neural network for segmenting neuroanatomy
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic
segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is …
segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is …
Deep learning for brain MRI segmentation: state of the art and future directions
Quantitative analysis of brain MRI is routine for many neurological diseases and conditions
and relies on accurate segmentation of structures of interest. Deep learning-based …
and relies on accurate segmentation of structures of interest. Deep learning-based …
Fastsurfer-a fast and accurate deep learning based neuroimaging pipeline
Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …
consuming optimization steps, and thus, do not scale well to large cohort studies with …
A 3D spatially weighted network for segmentation of brain tissue from MRI
The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid
diagnosis, treatment and tracking the progression of different neurologic diseases. Medical …
diagnosis, treatment and tracking the progression of different neurologic diseases. Medical …
Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder
HE Atlason, A Love, S Sigurdsson… - Medical Imaging …, 2019 - spiedigitallibrary.org
Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery (FLAIR) and
T2-weighted magnetic resonance images (MRIs) of the human brain are common in the …
T2-weighted magnetic resonance images (MRIs) of the human brain are common in the …
M3Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation
Segmentation of the brain into gray matter, white matter, and cerebrospinal fluid (CSF) using
magnetic resonance (MR) imaging plays a fundamental role in neuroimaging research and …
magnetic resonance (MR) imaging plays a fundamental role in neuroimaging research and …
3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads
The existing deep convolutional neural networks (DCNNs) based methods have achieved
significant progress regarding automatic glioma segmentation in magnetic resonance …
significant progress regarding automatic glioma segmentation in magnetic resonance …
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive
volumetric studies and quantitative analysis of early brain development. However …
volumetric studies and quantitative analysis of early brain development. However …
A lifelong learning approach to brain MR segmentation across scanners and protocols
Convolutional neural networks (CNNs) have shown promising results on several
segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs …
segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs …