# The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

arXiv: https://arxiv.org/abs/2305.07642 · HTML: https://mlsanity.com/paper/2305.07642/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 12 May 2023.

## Builds on (most cited first, 3 of 3)

- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation](https://mlsanity.com/paper/2201.02831/) (2022)

## Built on by (most cited first, 30 of 53)

- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [AutoRG-Brain: Grounded Report Generation for Brain MRI](https://mlsanity.com/paper/2407.16684/) (2024)
- [Multi-modal Vision Pre-training for Medical Image Analysis](https://mlsanity.com/paper/2410.10604/) (2024)
- [Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge](https://mlsanity.com/paper/2405.09787/) (2024)
- [Towards Scalable Language-Image Pre-training for 3D Medical Imaging](https://mlsanity.com/paper/2505.21862/) (2025)
- [BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023](https://mlsanity.com/paper/2407.08855/) (2024)
- [Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective](https://mlsanity.com/paper/2506.10142/) (2025)
- [A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning](https://mlsanity.com/paper/2506.14432/) (2025)
- [OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks](https://mlsanity.com/paper/2511.00846/) (2025)
- [Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging](https://mlsanity.com/paper/2409.08232/) (2024)
- [Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge](https://mlsanity.com/paper/2405.18383/) (2024)
- [Performance of GPT-5 in Brain Tumor MRI Reasoning](https://mlsanity.com/paper/2508.10865/) (2025)
- [Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks](https://mlsanity.com/paper/2312.07630/) (2023)
- [An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentation](https://mlsanity.com/paper/2402.07008/) (2024)
- [Robust Conformal Volume Estimation in 3D Medical Images](https://mlsanity.com/paper/2407.19938/) (2024)
- [Autoregressive Sequence Modeling for 3D Medical Image Representation](https://mlsanity.com/paper/2409.08691/) (2024)
- [Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation](https://mlsanity.com/paper/2411.04632/) (2024)
- [Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Code](https://mlsanity.com/paper/2501.18328/) (2025)
- [Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis](https://mlsanity.com/paper/2604.16729/) (2026)
- [BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning](https://mlsanity.com/paper/2604.27277/) (2026)
- [On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks](https://mlsanity.com/paper/2405.02852/) (2024)
- [Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)](https://mlsanity.com/paper/2311.14148/) (2023)
- [Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification](https://mlsanity.com/paper/2411.12874/) (2024)
- [GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRI](https://mlsanity.com/paper/2503.04325/) (2025)
- [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025)
- [Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2409.13229/) (2024)
- [RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features](https://mlsanity.com/paper/2507.08546/) (2025)
- [BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis](https://mlsanity.com/paper/2507.09036/) (2025)

## Abstract (arXiv)

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.