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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 151–200 of 436 papers

TitleStatusHype
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)—0
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI—0
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)Code2
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)—0
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via InpaintingCode1
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
Squeeze Excitation Embedded Attention UNet for Brain Tumor Segmentation—0
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma—0
Multiclass MRI Brain Tumor Segmentation using 3D Attention-based U-Net—0
Flexible Fusion Network for Multi-modal Brain Tumor Segmentation—0
Brain Tumor Segmentation from MRI Images using Deep Learning Techniques—0
3D Brainformer: 3D Fusion Transformer for Brain Tumor Segmentation—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism—0
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning—0
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning—0
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging SegmentationCode0
Unsupervised Brain Tumor Segmentation with Image-based Prompts—0
Medical Image Analysis using Deep Relational Learning—0
Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging—0
M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging—0
Multi-class Brain Tumor Segmentation using Graph Attention Network—0
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation—0
Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor SegmentationCode0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image AnalysisCode1
Scratch Each Other's Back: Incomplete Multi-Modal Brain Tumor Segmentation via Category Aware Group Self-Support LearningCode1
Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution—0
Towards fully automated deep-learning-based brain tumor segmentation: is brain extraction still necessary?Code0
M-GenSeg: Domain Adaptation For Target Modality Tumor Segmentation With Annotation-Efficient SupervisionCode0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans—0
Generative Adversarial Networks for Weakly Supervised Generation and Evaluation of Brain Tumor Segmentations on MR Images—0
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images—0
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic ModelCode3
Brain Tumor Segmentation using Enhanced U-Net Model with Empirical AnalysisCode0
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network—0
Deep Superpixel Generation and Clustering for Weakly Supervised Segmentation of Brain Tumors in MR Images—0
Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationCode1
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor SegmentationCode1
SFusion: Self-attention based N-to-One Multimodal Fusion BlockCode1
Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning—0
Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation—0
Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation—0
PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge DistillationCode1
Analyzing Deep Learning Based Brain Tumor Segmentation with Missing MRI Modalities—0
A Transformer-based Generative Adversarial Network for Brain Tumor Segmentation—0
PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training—0
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