Brain Tumor Segmentation
126 papers with code • 9 benchmarks • 4 datasets
Brain Tumor Segmentation is a medical image analysis task that involves the separation of brain tumors from normal brain tissue in magnetic resonance imaging (MRI) scans. The goal of brain tumor segmentation is to produce a binary or multi-class segmentation map that accurately reflects the location and extent of the tumor.
( Image credit: Brain Tumor Segmentation with Deep Neural Networks )
Benchmarks
These leaderboards are used to track progress in Brain Tumor Segmentation
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Libraries
Use these libraries to find Brain Tumor Segmentation models and implementations
BraTS 2017
BraTS 2015
BraTS 2013
BraTS 2014
Most implemented papers
Attention U-Net: Learning Where to Look for the Pancreas
ozan-oktay/Attention-Gated-Networks
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We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Brain Tumor Segmentation with Deep Neural Networks
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
charan223/Brain-Tumor-Segmentation-using-Topological-Loss
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A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
pykao/Modified-3D-UNet-Pytorch
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Quantitative analysis of brain tumors is critical for clinical decision making.
3D MRI brain tumor segmentation using autoencoder regularization
black0017/MedicalZooPytorch
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Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
mateuszbuda/brain-segmentation
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Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors.
nnU-Net for Brain Tumor Segmentation
MIC-DKFZ/nnunet
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We apply nnU-Net to the segmentation task of the BraTS 2020 challenge.
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
shu-hai/two-stage-VAE-Attention-gate-BraTS2020
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Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning.
TBraTS: Trusted Brain Tumor Segmentation
cocofeat/tbrats
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In our method, uncertainty is modeled explicitly using subjective logic theory, which treats the predictions of backbone neural network as subjective opinions by parameterizing the class probabilities of the segmentation as a Dirichlet distribution.
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
