Brain Segmentation
60 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Brain Segmentation
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Libraries
Use these libraries to find Brain Segmentation models and implementations
CrossMoDA
BRATS 2021
Tc1 Mouse cerebellum atlas
Multi-template MRI mouse brain atlas
Most implemented papers
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.
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.
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.
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
ai-med/squeeze_and_excitation
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Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
Importance Driven Continual Learning for Segmentation Across Domains
MECLabTUDA/ACS
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The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications.
Partial supervision for the FeTA challenge 2021
Label-set loss functions allow to train deep neural networks with partially segmented images, i. e. segmentations in which some classes may be grouped into super-classes.
FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain
A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions.
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.
3D Densely Convolutional Networks for VolumetricSegmentation
black0017/MedicalZooPytorch
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arXiv preprint 2017
The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network.
Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network
Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions.
