Brain Tumor Segmentation
Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.
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Greatest papers with code
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.
SOTA for Lesion Segmentation on ISLES-2015
3D MEDICAL IMAGING SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN TUMOR SEGMENTATION LESION SEGMENTATION
Brain Tumor Segmentation with Deep Neural Networks
• naldeborgh7575/brain_segmentation
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
• taigw/brats17
• 
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.
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.
#2 best model for
Brain Tumor Segmentation
on BRATS-2015
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
• iNLyze/DeepLearning-SeGAN-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.
3D MRI brain tumor segmentation using autoencoder regularization
• IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization
Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease.
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
• Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN
•
Quantitative analysis of brain tumors is critical for clinical decision making.
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.
BRAIN TUMOR SEGMENTATION INTERACTIVE SEGMENTATION PLACENTA SEGMENTATION SEMANTIC SEGMENTATION
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
• milliondegree/semantic-segmentation-tensorflow
•
Since the proposal of fully convolutional neural network (FCNN), it has been widely used in semantic segmentation because of its high accuracy of pixel-wise classification as well as high precision of localization.
BRAIN TUMOR SEGMENTATION INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION
Glioma Segmentation with Cascaded Unet
• lachinov/brats2018-graphlabunn
• 
MRI analysis takes central position in brain tumor diagnosis and treatment, thus it's precise evaluation is crucially important.