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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 71–80 of 436 papers

TitleStatusHype
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Deep Learning-Based Concurrent Brain Registration and Tumor SegmentationCode1
Demystifying Brain Tumour Segmentation Networks: Interpretability and Uncertainty AnalysisCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image SegmentationCode1
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
Extending nn-UNet for brain tumor segmentationCode1
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Deep Learning Based Brain Tumor Segmentation: A SurveyCode1
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