# Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

arXiv: https://arxiv.org/abs/2405.09787 · HTML: https://mlsanity.com/paper/2405.09787/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 10 of 10)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training](https://mlsanity.com/paper/2304.06716/) (2023)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma](https://mlsanity.com/paper/2305.07642/) (2023)
- [GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging](https://mlsanity.com/paper/2103.01006/) (2021)
- [Preoperative brain tumor imaging: models and software for segmentation and standardized reporting](https://mlsanity.com/paper/2204.14199/) (2022)

## Built on by (most cited first, 15 of 15)

- [The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI](https://mlsanity.com/paper/2405.18368/) (2024)
- [BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification](https://mlsanity.com/paper/2506.14318/) (2025)
- [BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023](https://mlsanity.com/paper/2407.08855/) (2024)
- [Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge](https://mlsanity.com/paper/2405.18383/) (2024)
- [Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation](https://mlsanity.com/paper/2411.04632/) (2024)
- [Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI Synthesis](https://mlsanity.com/paper/2411.14684/) (2024)
- [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025)
- [Post-Processing Methods for Improving Accuracy in MRI Inpainting](https://mlsanity.com/paper/2510.15282/) (2025)
- [A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development](https://mlsanity.com/paper/2510.20196/) (2025)
- [Knowledge-Guided Brain Tumor Segmentation via Synchronized Visual-Semantic-Topological Prior Fusion](https://mlsanity.com/paper/2507.08574/) (2025)
- [Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble](https://mlsanity.com/paper/2512.14648/) (2025)
- [HD-TTA: Hypothesis-Driven Test-Time Adaptation for Safer Brain Tumor Segmentation](https://mlsanity.com/paper/2602.19454/) (2026)
- [Recover Semantics First, Generate Better: Improved Latent Modeling for 3D MRI Reconstruction and Cross-Contrast Synthesis](https://mlsanity.com/paper/2606.17989/) (2026)
- [Sharpening the Ensemble: An SSIM-Aligned Residual Refiner for Brain-MRI Inpainting Post-Processing](https://mlsanity.com/paper/2609.03981/) (2026)
- [Dataset-Specific Encoding of Image Content Underlies Cross-Dataset Failure in Brain MRI Foundation Models](https://mlsanity.com/paper/2610.13027/) (2026)

## Abstract (arXiv)

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.