mlsanityData to 7 Oct 2026

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv 2305.17033 ↗First posted 26 May 2023eess.IV cs.CV cs.LGcited by 49

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Abstract (arXiv)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 3 of its 34 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

3 of 34 references.

References · 3

  1. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  2. 2021The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classificationcited by 507
  3. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 20; 2025: 17; 2026: 8.

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  1. 2024SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentationcited by 266
  2. 2024BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at oncecited by 107
  3. 2024The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRIcited by 65
  4. 2023Large-Vocabulary Segmentation for Medical Images with Text Promptscited by 60
  5. 2024Foundation Models for Biomedical Image Segmentation: A Surveycited by 28
  6. 2024Cross-conditioned Diffusion Model for Medical Image to Image Translationcited by 26
  7. 2024AutoRG-Brain: Grounded Report Generation for Brain MRIcited by 23
  8. 2024Multi-modal Vision Pre-training for Medical Image Analysiscited by 22
  9. 2025Towards Scalable Language-Image Pre-training for 3D Medical Imagingcited by 13
  10. 2024xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpartcited by 12
  11. 2024Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Taskscited by 11
  12. 2024BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023cited by 11
37 more
  1. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  2. 2024Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imagingcited by 7
  3. 2025A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learningcited by 7
  4. 2024Federated brain tumor segmentation: an extensive benchmarkcited by 5
  5. 2024Autoregressive Sequence Modeling for 3D Medical Image Representationcited by 4
  6. 2026BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learningcited by 4
  7. 2024On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networkscited by 3
  8. 2024Domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentationcited by 3
  9. 2024Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentationcited by 3
  10. 2025SAM-aware Test-time Adaptation for Universal Medical Image Segmentationcited by 3
  11. 2025MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentationcited by 3
  12. 2023Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)cited by 2
  13. 2025GBT-SAM: A Parameter-Efficient Depth-Aware Model for Generalizable Brain tumour Segmentation on mp-MRIcited by 2
  14. 2025WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentationcited by 2
  15. 2024Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor Segmentationcited by 1
  16. 2024A New Logic For Pediatric Brain Tumor Segmentationcited by 1
  17. 2025Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flowscited by 1
  18. 2023Automated ensemble method for pediatric brain tumor segmentationcited by 0
  19. 2023Tumor-Centered Patching for Enhanced Medical Image Segmentationcited by 0
  20. 2024Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNetcited by 0
  21. 2024SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentationcited by 0
  22. 2025Automated segmentation of pediatric neuroblastoma on multi-modal MRI: Results of the SPPIN challenge at MICCAI 2023cited by 0
  23. 2025AI-Driven MRI-based Brain Tumour Segmentation Benchmarkingcited by 0
  24. 2025A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scanscited by 0
  25. 2025SSFMamba: Learning Symmetry-driven Spatial-Frequency Modeling for Physically Consistent 3D Medical Image Segmentationcited by 0
  26. 2025Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDAcited by 0
  27. 2025Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancementscited by 0
  28. 2025From Healthy Scans to Annotated Tumors: A Tumor Fabrication Framework for 3D Brain MRI Synthesiscited by 0
  29. 2025Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemblecited by 0
  30. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  31. 2026Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imagingcited by 0
  32. 2026SegGuidedNet: Sub-Region-Aware Attention Supervision for Interpretable Brain Tumor Segmentationcited by 0
  33. 2026BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentationcited by 0
  34. 2026Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRIcited by 0
  35. 2026A continually expandable foundation model for brain MRIcited by 0
  36. 2026Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformationscited by 0
  37. 2026NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRIcited by 0