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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 51–100 of 436 papers

TitleStatusHype
Enhancing Brain Tumor Classification Using TrAdaBoost and Multi-Classifier Deep Learning Approaches—0
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation—0
Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade—0
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement—0
Federated brain tumor segmentation: an extensive benchmark—0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor SegmentationCode1
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
Fed-MUnet: Multi-modal Federated Unet for Brain Tumor SegmentationCode1
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection—0
Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection—0
Anatomical Consistency Distillation and Inconsistency Synthesis for Brain Tumor Segmentation with Missing Modalities—0
Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Decoupling Feature Representations of Ego and Other Modalities for Incomplete Multi-modal Brain Tumor SegmentationCode0
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor SegmentationCode0
Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIsCode0
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023—0
Brain Tumor Segmentation in MRI Images with 3D U-Net and Contextual Transformer—0
SimTxtSeg: Weakly-Supervised Medical Image Segmentation with Simple Text CuesCode1
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans—0
Unsupervised Domain Adaptation for Pediatric Brain Tumor SegmentationCode1
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI SynthesisCode1
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
BrainSegFounder: Towards 3D Foundation Models for Neuroimage SegmentationCode1
Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal DependencyCode0
Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning—0
Interactive Image Selection and Training for Brain Tumor Segmentation Network—0
Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation—0
Dealing with All-stage Missing Modality: Towards A Universal Model with Robust Reconstruction and Personalization—0
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI—0
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform RegressionCode1
Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation—0
Patient-Specific Real-Time Segmentation in Trackerless Brain UltrasoundCode0
Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing DataCode0
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural NetworksCode0
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)—0
A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete ModalitiesCode0
SegFormer3D: an Efficient Transformer for 3D Medical Image SegmentationCode3
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor ClassificationCode0
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation—0
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches—0
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection—0
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 ChallengeCode0
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection—0
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