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Found 14 papers, 7 papers with code
VINNA for Neonates -- Orientation Independence through Latent Augmentations
However, these transformations in the image space still require resampling, reducing accuracy specifically in the context of label interpolation.
FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent structures on high-resolutional brain MRI
The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioural, and cognitive functions.
Estimating Head Motion from MR-Images
Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed.
An automated, geometry-based method for hippocampal shape and thickness analysis
In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature.
Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions
We analyse how this resolution-bias in the data distribution propagates to systematically biased predictions for group L at higher resolutions.
FastSurferVINN: Building Resolution-Independence into Deep Learning Segmentation Methods -- A Solution for HighRes Brain MRI
Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1. 0 mm for improved structure definition and morphometry.
Rapid head-pose detection for automated slice prescription of fetal-brain MRI
In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment.
Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI
The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function.
Learning Anatomical Segmentations for Tractography from Diffusion MRI
Here, we introduce fast, deep learning-based segmentation of 170 anatomical regions directly on diffusion-weighted MR images, removing the dependency of conventional segmentation methods on T 1-weighted images and slow pre-processing pipelines.
FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation.
FatSegNet : A Fully Automated Deep Learning Pipeline for Adipose Tissue Segmentation on Abdominal Dixon MRI
Purpose: Development of a fast and fully automated deep learning pipeline (FatSegNet) to accurately identify, segment, and quantify abdominal adipose tissue on Dixon MRI from the Rhineland Study - a large prospective population-based study.
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation.
Complex Fully Convolutional Neural Networks for MR Image Reconstruction
Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI).
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.
