mlsanityData to 7 Oct 2026

BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

arXiv 2407.08855 ↗First posted 11 Jul 2024eess.IV cs.CVcited by 11

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Abstract (arXiv)

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 56 references that are in the map. First posted on arXiv 11 Jul 2024.

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12 of 56 references.

References · 12

  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. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88
  4. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  5. 2023The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningiomacited by 51
  6. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)cited by 49
  7. 2021GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imagingcited by 25
  8. 2023The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)cited by 24
  9. 2024Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challengecited by 14
  10. 2024Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Taskscited by 11
  11. 2024Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumorscited by 6
  12. 2023Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)cited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 6.

11 papers.

  1. 2025In the Picture: Medical Imaging Datasets, Artifacts, and their Living Reviewcited by 8
  2. 2025Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growthcited by 3
  3. 2025Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy studycited by 2
  4. 2024A New Logic For Pediatric Brain Tumor Segmentationcited by 1
  5. 2025Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performancecited by 0
  6. 2026HD-TTA: Hypothesis-Driven Test-Time Adaptation for Safer Brain Tumor Segmentationcited by 0
  7. 2026VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentationcited by 0
  8. 2026How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Modelscited by 0
  9. 2026Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRIcited by 0
  10. 2026A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imagingcited by 0
  11. 2026NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRIcited by 0