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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 361–370 of 436 papers

TitleStatusHype
Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net—0
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans—0
Brain Tumor Segmentation and Survival Prediction—0
Quantitative Impact of Label Noise on the Quality of Segmentation of Brain Tumors on MRI scans—0
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
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation—0
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