mlsanityData to 14 Oct 2026

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

arXiv 2305.07642 ↗First posted 12 May 2023cs.CV cs.AI cs.LGcited by 53

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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.

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

3 of 35 references.

References · 3

  1. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 518
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  3. 2022CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentationcited by 58

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 17; 2025: 18; 2026: 14.

53 papers.

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  1. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 69
  2. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 62
  3. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  4. 2024AutoRG-Brain: Grounded Report Generation for Brain MRIcited by 24
  5. 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 23
  6. 2024Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challengecited by 15
  7. 2025Towards Scalable Language-Image Pre-training for 3D Medical Imagingcited by 14
  8. 2024BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023cited by 11
  9. 2025Rethinking Brain Tumor Segmentation from the Frequency Domain Perspectivecited by 9
  10. 2025A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learningcited by 8
  11. 2025OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Taskscited by 8
  12. 2024Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imagingcited by 7
38 more
  1. 2024Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challengecited by 5
  2. 2025Performance of GPT-5 in Brain Tumor MRI Reasoningcited by 5
  3. 2023Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networkscited by 4
  4. 2024An Optimization Framework for Processing and Transfer Learning for the Brain Tumor Segmentationcited by 4
  5. 2024Robust Conformal Volume Estimation in 3D Medical Imagescited by 4
  6. 2024Autoregressive Sequence Modeling for 3D Medical Image Representationcited by 4
  7. 2024Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentationcited by 4
  8. 2025Virtual Full-stack Scanning of Brain MRI via Imputing Any Quantised Codecited by 4
  9. 2026Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysiscited by 4
  10. 2026BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learningcited by 4
  11. 2024On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networkscited by 3
  12. 2023Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)cited by 2
  13. 2024Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classificationcited by 2
  14. 2025GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRIcited by 2
  15. 2025The Missing Piece: A Case for Pre-Training in 3D Medical Object Detectioncited by 2
  16. 2024Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentationcited by 1
  17. 2025RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Featurescited by 1
  18. 2025BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysiscited by 1
  19. 2025BARL: Bilateral Alignment in Representation and Label Spaces for Semi-Supervised Volumetric Medical Image Segmentationcited by 1
  20. 2025A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Developmentcited by 1
  21. 2026Visual Instruction-Finetuned Language Model for Versatile Brain MR Image Taskscited by 1
  22. 2026Cross Modality Image Translation In Medical Imaging Using Generative Frameworkscited by 1
  23. 2023ReFuSeg: Regularized Multi-Modal Fusion for Precise Brain Tumour Segmentationcited by 0
  24. 2024Cross-D Conv: Cross-Dimensional Transferable Knowledge Base via Fourier Shifting Operationcited by 0
  25. 2024SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentationcited by 0
  26. 2025Mix-modal Federated Learning for MRI Image Segmentationcited by 0
  27. 2025Towards Label-Free Brain Tumor Segmentation: Unsupervised Learning with Multimodal MRIcited by 0
  28. 2025Efficient Meningioma Tumor Segmentation Using Ensemble Learningcited by 0
  29. 2025Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemblecited by 0
  30. 2025A unified FLAIR hyperintensity segmentation model for various CNS tumor types and acquisition time pointscited by 0
  31. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  32. 2026Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imagingcited by 0
  33. 2026D3Seg: Dependency-Aware Diffusion for Brain Tumor Segmentation with Missing Modalitiescited by 0
  34. 2026Multi-LLM Collaborative MRI Report Generation for Visual Instruction Tuning in Brain Oncologycited by 0
  35. 2026PerFact: Perception-Derived Fact Prompting for 3D Brain MRI Report Generationcited by 0
  36. 2026MOSAIC: Modality-agnostic Spectral Alignment for Federated Image-level Weakly Supervised Tumor Segmentation under Client-specific Missing Modalitiescited by 0
  37. 2026BrainDiff: Longitudinal Report Generation for Multimodal Brain MRIcited by 0
  38. 2026Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformationscited by 0

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