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
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time.
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.
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
DLTK/models
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NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
ibm/multimodal-3d-image-segmentation
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In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures.
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
arnab39/FewShot_GAN-Unet3D
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In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.
Knowing what you know in brain segmentation using Bayesian deep neural networks
In this paper, we describe a Bayesian deep neural network (DNN) for predicting FreeSurfer segmentations of structural MRI volumes, in minutes rather than hours.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
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.
DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
NYUMedML/DARTS
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This is also the first work to include an expert reader study to assess the quality of the segmentation obtained using a deep-learning-based model.
End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation
However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability.
