Browse > Medical > Medical Image Segmentation > Brain Tumor Segmentation

Brain Tumor Segmentation Edit

12 papers with code · Medical

Brain tumor segmentation is the task of segmenting tumors from other brain artefacts in MRI image of the brain.

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Brain Tumor Segmentation with Deep Neural Networks

13 May 2015naldeborgh7575/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.

177

Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

1 Sep 2017taigw/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.

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Autofocus Layer for Semantic Segmentation

22 May 2018yaq007/Autofocus-Layer

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.

118

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

18 Mar 2016yaq007/Autofocus-Layer

We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.

118

SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation

6 Jun 2017iNLyze/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.

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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation

3 Jul 2017taigw/geodesic_distance

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.

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Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge

28 Feb 2018Jack-Etheredge/Brain-Tumor-Segmentation-3D-UNet-CNN

Quantitative analysis of brain tumors is critical for clinical decision making.

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Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

25 Dec 2017milliondegree/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.

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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.

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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

5 Nov 2018christophbrgr/brats-orchestra

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

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