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

arXiv: https://arxiv.org/abs/2407.08855 · HTML: https://mlsanity.com/paper/2407.08855/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [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)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma](https://mlsanity.com/paper/2305.07642/) (2023)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)](https://mlsanity.com/paper/2305.17033/) (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)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)](https://mlsanity.com/paper/2305.09011/) (2023)
- [Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge](https://mlsanity.com/paper/2405.09787/) (2024)
- [Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks](https://mlsanity.com/paper/2403.09262/) (2024)
- [Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors](https://mlsanity.com/paper/2401.08404/) (2024)
- [Automated 3D Tumor Segmentation using Temporal Cubic PatchGAN (TCuP-GAN)](https://mlsanity.com/paper/2311.14148/) (2023)

## Built on by (most cited first, 11 of 11)

- [In the Picture: Medical Imaging Datasets, Artifacts, and their Living Review](https://mlsanity.com/paper/2501.10727/) (2025)
- [Mechanistic Learning with Guided Diffusion Models to Predict Spatio-Temporal Brain Tumor Growth](https://mlsanity.com/paper/2509.09610/) (2025)
- [Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study](https://mlsanity.com/paper/2508.16650/) (2025)
- [A New Logic For Pediatric Brain Tumor Segmentation](https://mlsanity.com/paper/2411.01390/) (2024)
- [Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performance](https://mlsanity.com/paper/2512.19707/) (2025)
- [HD-TTA: Hypothesis-Driven Test-Time Adaptation for Safer Brain Tumor Segmentation](https://mlsanity.com/paper/2602.19454/) (2026)
- [VISTA: Variance-Gated Inter-Sequence Test-Time Adaptation for Multi-Sequence MRI Segmentation](https://mlsanity.com/paper/2605.17433/) (2026)
- [How Much MRI Preprocessing Is Enough? A Cost-Utility Study for Brain MRI Foundation Models](https://mlsanity.com/paper/2606.08164/) (2026)
- [Diffusion-Refined Segmentation and Vision-Language Interpretation for Pediatric Brain Tumor MRI](https://mlsanity.com/paper/2606.14072/) (2026)
- [A Unified Benchmark of Deep Learning Models for Multi-task 3D Brain Tumor Segmentation from Magnetic Resonance Imaging](https://mlsanity.com/paper/2607.28858/) (2026)
- [NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI](https://mlsanity.com/paper/2609.16873/) (2026)

## 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.