The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2014/12/04 by Bjoern Menze, Bjoern H. Menze, András Jakab +85 · 402 citations
Computer Science · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Medical Image Segmentation Techniques
paper · pdf · doi:10.1109/tmi.2014.2377694
openalex publication_date 2014/12/04 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/30
Abstract
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
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- FermatSyn: SAM2-Enhanced Bidirectional Mamba with Isotropic Spiral Scanning for Multi-Modal Medical Image Synthesis
- The Method of Multimodal MRI Brain Image Segmentation Based on Differential Geometric Features
- Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review
- 2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation
- Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization
- Federated Learning for Cyber Physical Systems: A Comprehensive Survey
- How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model
- Regression is all you need for medical image translation
- Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis
- Confident but Unreliable: A Behavioral Safety Audit of Vision-Language Models on Brain MRI
- Automated MRI based pipeline for glioma segmentation and prediction of grade, IDH mutation and 1p19q co-deletion
- Towards safe deep learning: accurately quantifying biomarker uncertainty in neural network predictions
- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
- UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation
- FLIM-based Salient Object Detection Networks with Adaptive Decoders
- LymphAtlas- A Unified Multimodal Lymphoma Imaging Repository Delivering AI-Enhanced Diagnostic Insight
- VERITAS: A Multi-Agent Co-Scientist for Verifiable Image-Derived Hypothesis Testing
- MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution
- NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives
- Dilated Inception U-Net (DIU-Net) for Brain Tumor Segmentation
- Convolutional 3D to 2D Patch Conversion for Pixel-Wise Glioma Segmentation in MRI Scans
- Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
- DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
- CALF: A Conditionally Adaptive Loss Function to Mitigate Class-Imbalanced Segmentation
- DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation
- Three‐dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre‐operative multimodal MR images
- TBI contusion segmentation from MRI using convolutional neural networks
- Iterative multi-path tracking for video and volume segmentation with\n sparse point supervision
- DR-Unet104 for Multimodal MRI Brain Tumor Segmentation
- Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation
- Synthesis of brain tumor multicontrast MR images for improved data augmentation
- A unified representation network for segmentation with missing modalities
- CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation
- Deep Learning-based Type Identification of Volumetric MRI Sequences
- Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
- DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption
- A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images
- Evaluation of multislice inputs to convolutional neural networks for medical image segmentation
- Text-Guided Refinement of Multi-sequence Glioma Subregion Segmentation with a Vision-Language Foundation Model
- Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models
- Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)
- Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance
- Multi-Modal Brain Tumor Segmentation via 3D Multi-Scale Self-attention and Cross-attention
- Large Scale Supervised Pretraining For Traumatic Brain Injury Segmentation
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