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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 426–436 of 436 papers

TitleStatusHype
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 with Deep Neural NetworksCode1
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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