journal cover

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy

NeuroImage

Feb 23, 2017

Christian WachingerTassilo Klein

Abstract

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi...read more

Mentioned in this Paper

Health Center
Biological Neural Networks
Biologic Segmentation
Classification
Magnetic Resonance Imaging
Three-dimensional
Spatial Distribution
Brain
Neuroanatomy
Reversal Learning
18
338
Paper Details
References
  • References2
  • Citations12
1
  • References2
  • Citations12
12

Similar Papers Found In These Feeds

Computer Vision in Medicine

Computer Vision in medicine has shown great application in surgery and therapy of some diseases. Discover the latest research on Computer Vision in this feed.

Magnetic Resonance Imaging: Brain

Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique that uses a powerful magnetic field and radio waves to generate images of the area of interest. MRI of the brain is commonly performed to visualize the brain and related structures in detail. Discover the latest research of MRI and the brain here.

Neural Networks

Here is the latest research on physiological and in silico networks and circuits implicated in the nervous system.

Neural Dynamics in Primates

Here is the latest research on neuroelectrophysiology and thalmo-cortical and neural dynamics in primates.

Related Papers

Medical Image Computing and Computer-assisted Intervention : MICCAI

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network

Medical Image Computing and Computer-assisted Intervention : MICCAIMarch 1, 2014
Adhish PrasoonMads Nielsen
© 2020 Meta ULC. All rights reserved

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy

NeuroImage

Feb 23, 2017

Christian WachingerTassilo Klein

PMID: 28223187

DOI: 10.1016/j.neuroimage.2017.02.035

Abstract

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi...read more

Mentioned in this Paper

Health Center
Biological Neural Networks
Biologic Segmentation
Classification
Magnetic Resonance Imaging
Three-dimensional
Spatial Distribution
Brain
Neuroanatomy
Reversal Learning
18
338

Similar Papers Found In These Feeds

Computer Vision in Medicine

Computer Vision in medicine has shown great application in surgery and therapy of some diseases. Discover the latest research on Computer Vision in this feed.

Magnetic Resonance Imaging: Brain

Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique that uses a powerful magnetic field and radio waves to generate images of the area of interest. MRI of the brain is commonly performed to visualize the brain and related structures in detail. Discover the latest research of MRI and the brain here.

Related Papers

Medical Image Computing and Computer-assisted Intervention : MICCAI

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network

Medical Image Computing and Computer-assisted Intervention : MICCAIMarch 1, 2014
Adhish PrasoonMads Nielsen
Paper Details
References
  • References2
  • Citations12
1
  • References2
  • Citations12
12
/papers/deepnat-deep-convolutional-neural-network-for/28223187