Brain Tumor Segmentation
91 papers with code • 9 benchmarks • 5 datasets
Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.
( 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 2018
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.
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.
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.
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.
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
