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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 371–380 of 436 papers

TitleStatusHype
3D U-Net Based Brain Tumor Segmentation and Survival Days PredictionCode0
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks—0
Prediction of Overall Survival of Brain Tumor Patients—0
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation—0
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
End-to-End Boundary Aware Networks for Medical Image Segmentation—0
Multi-step Cascaded Networks for Brain Tumor SegmentationCode0
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems—0
A Structural Graph-Based Method for MRI Analysis—0
Robustifying deep networks for image segmentation—0
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