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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 201–250 of 436 papers

TitleStatusHype
High-Resolution Swin Transformer for Automatic Medical Image SegmentationCode1
Adaptive Active Contour Model for Brain Tumor SegmentationCode0
Single MR Image Super-Resolution using Generative Adversarial NetworkCode1
Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand—0
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation—0
TBraTS: Trusted Brain Tumor SegmentationCode1
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
CTVR-EHO TDA-IPH Topological Optimized Convolutional Visual Recurrent Network for Brain Tumor Segmentation and Classification—0
mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor SegmentationCode1
A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation—0
Class Balanced PixelNet for Neurological Image Segmentation—0
SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalitiesCode1
UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image SegmentationCode3
Category Guided Attention Network for Brain Tumor Segmentation in MRICode0
Translation Consistent Semi-supervised Segmentation for 3D Medical ImagesCode1
ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation—0
Self Pre-training with Masked Autoencoders for Medical Image Classification and SegmentationCode1
Artificial Intelligence Solution for Effective Treatment Planning for Glioblastoma Patients—0
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion—0
Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network—0
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image SegmentationCode1
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationCode0
A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation—0
HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging—0
TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical ImagesCode2
Self-semantic contour adaptation for cross modality brain tumor segmentation—0
Optimizing Prediction of MGMT Promoter Methylation from MRI Scans using Adversarial Learning—0
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation TaskCode1
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation—0
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI ImagesCode2
Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation—0
Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challengeCode1
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking ResultsCode0
ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network—0
Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRICode1
Extending nn-UNet for brain tumor segmentationCode1
Diffusion Models for Implicit Image Segmentation EnsemblesCode1
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation—0
Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning—0
A Robust Volumetric Transformer for Accurate 3D Tumor SegmentationCode1
Non Parametric Data Augmentations Improve Deep-Learning based Brain Tumor Segmentation—0
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities—0
A Tri-attention Fusion Guided Multi-modal Segmentation Network—0
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs—0
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image SegmentationCode0
Combining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation With Self-Supervised Pretraining—0
Optimized U-Net for Brain Tumor SegmentationCode0
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 ChallengeCode1
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Utilizing Attention, Linked Blocks, And Pyramid Pooling To Propel Brain Tumor Segmentation In 3DCode0
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