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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 301–310 of 436 papers

TitleStatusHype
Inter-slice Context Residual Learning for 3D Medical Image SegmentationCode1
Efficient embedding network for 3D brain tumor segmentation—0
SoftSeg: Advantages of soft versus binary training for image segmentation—0
Automatic Brain Tumor Segmentation with Scale Attention NetworkCode0
DR-Unet104 for Multimodal MRI brain tumor segmentationCode1
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationCode1
Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challengeCode1
nnU-Net for Brain Tumor SegmentationCode1
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solutionCode1
Brain Tumor Segmentation Network Using Attention-based Fusion and Spatial Relationship Constraint—0
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