Brain Segmentation
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Unsupervised deep learning for Bayesian brain MRI segmentation
Unsupervised Learning for Image Registration
BRAIN IMAGE SEGMENTATION BRAIN SEGMENTATION FEW-SHOT IMAGE SEGMENTATION IMAGE REGISTRATION ZERO SHOT SEGMENTATION
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
To the best of our knowledge, our work is the first to study subcortical structure segmentation on such large-scale and heterogeneous data.
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
• ai-med/squeeze_and_excitation
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Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
BRAIN SEGMENTATION IMAGE CLASSIFICATION SEMANTIC SEGMENTATION
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
3D Densely Convolutional Networks for Volumetric Segmentation
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
BRAIN SEGMENTATION INFANT BRAIN MRI SEGMENTATION SEMANTIC SEGMENTATION
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging
• NIC-VICOROB/tissue_segmentation_comparison
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Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has attracted the attention of medical doctors and researchers since variations in tissue volume help in diagnosing and monitoring neurological diseases.
On Direct Distribution Matching for Adapting Segmentation Networks
We juxtapose our approach to state-of-the-art segmentation adaptation via adversarial training in the network-output space.