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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
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
RobU-Net: a heuristic robust multi-class brain tumor segmentation approaches for MRI scans—0
CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset—0
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
Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation—0
The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI—0
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
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
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor SegmentationCode0
BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives—0
Modality-Aware and Shift Mixer for Multi-modal Brain Tumor Segmentation—0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Self-calibrated convolution towards glioma segmentation—0
A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network—0
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser—0
Decentralized Gossip Mutual Learning (GML) for brain tumor segmentation on multi-parametric MRI—0
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor SegmentationCode0
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessmentCode0
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet—0
Using Singular Value Decomposition in a Convolutional Neural Network to Improve Brain Tumor Segmentation Accuracy—0
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images—0
Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty PredictionCode0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)—0
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks—0
Hybrid-Fusion Transformer for Multisequence MRICode0
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation—0
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
Whole-brain radiomics for clustered federated personalization in brain tumor segmentationCode0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
Image-level supervision and self-training for transformer-based cross-modality tumor segmentation—0
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