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Brain Tumor Segmentation

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 )

Papers

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Showing 401–436 of 436 papers

TitleStatusHype
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation—0
A Volumetric Convolutional Neural Network for Brain Tumor Segmentation—0
3D MRI brain tumor segmentation using autoencoder regularizationCode0
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation—0
Deep Recurrent Level Set for Segmenting Brain Tumors—0
Glioma Segmentation with Cascaded UnetCode0
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images—0
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation—0
Focus, Segment and Erase: An Efficient Network for Multi-Label Brain Tumor Segmentation—0
FR-MRInet: A Deep Convolutional Encoder-Decoder for Brain Tumor Segmentation with Relu-RGB and Sliding-windowCode0
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival PredictionCode0
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRICode0
Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks—0
Autofocus Layer for Semantic SegmentationCode0
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice LossCode0
Segmenting Brain Tumors with Symmetry—0
Automated Tumor Segmentation and Brain Mapping for the Tumor Area—0
A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation—0
Sequential 3D U-Nets for Biologically-Informed Brain Tumor Segmentation—0
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural NetworksCode0
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network—0
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image SegmentationCode0
SegAN: Adversarial Network with Multi-scale L_1 Loss for Medical Image SegmentationCode0
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures—0
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks—0
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network—0
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation—0
CNN-based Segmentation of Medical Imaging DataCode0
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion SegmentationCode0
Within-Brain Classification for Brain Tumor Segmentation—0
Brain Tumor Segmentation: A Comparative Analysis—0
Brain Tumor Detection Based On Mathematical Analysis and Symmetry Information—0
A Novel Method for Automatic Segmentation of Brain Tumors in MRI Images—0
Brain Tumor Detection Based On Symmetry Information—0
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