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

arXiv: https://arxiv.org/abs/2404.15009 · HTML: https://mlsanity.com/paper/2404.15009/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [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)
- [Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors](https://mlsanity.com/paper/2401.08404/) (2024)

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond](https://mlsanity.com/paper/2410.02362/) (2024)
- [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025)
- [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025)
- [Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRI](https://mlsanity.com/paper/2509.25889/) (2025)
- [Segmentation of Pediatric Brain Tumors using a Radiologically informed, Deep Learning Cascade](https://mlsanity.com/paper/2410.14020/) (2024)
- [A New Logic For Pediatric Brain Tumor Segmentation](https://mlsanity.com/paper/2411.01390/) (2024)
- [Enhancing efficiency in paediatric brain tumour segmentation using a pathologically diverse single-center clinical dataset](https://mlsanity.com/paper/2507.22152/) (2025)
- [Towards Reliable Pediatric Brain Tumor Segmentation: Task-Specific nnU-Net Enhancements](https://mlsanity.com/paper/2511.00449/) (2025)
- [An Explainable AI-Driven Framework for Automated Brain Tumor Segmentation Using an Attention-Enhanced U-Net](https://mlsanity.com/paper/2603.23344/) (2026)
- [Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis](https://mlsanity.com/paper/2604.07180/) (2026)
- [BiSegMamba: Efficient Bidirectional Tri-Oriented Mamba for 3D Medical Image Segmentation](https://mlsanity.com/paper/2605.30972/) (2026)
- [Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations](https://mlsanity.com/paper/2609.02600/) (2026)
- [NeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI](https://mlsanity.com/paper/2609.16873/) (2026)
- [nnFoundation: 3D Foundation Models for Radiology](https://mlsanity.com/paper/2609.26924/) (2026)

## 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. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 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.