# 3D Graph Attention Networks for High Fidelity Pediatric Glioma Segmentation

arXiv: https://arxiv.org/abs/2412.06743 · HTML: https://mlsanity.com/paper/2412.06743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [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)
- [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)
- [Multi-class Brain Tumor Segmentation using Graph Attention Network](https://mlsanity.com/paper/2302.05598/) (2023)
- [Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics](https://mlsanity.com/paper/2411.15872/) (2024)

## Abstract (arXiv)

Pediatric brain tumors, particularly gliomas, represent a significant cause of cancer related mortality in children with complex infiltrative growth patterns that complicate treatment. Early, accurate segmentation of these tumors in neuroimaging data is crucial for effective diagnosis and intervention planning. This study presents a novel 3D UNet architecture with a spatial attention mechanism tailored for automated segmentation of pediatric gliomas. Using the BraTS pediatric glioma dataset with multiparametric MRI data, the proposed model captures multi-scale features and selectively attends to tumor relevant regions, enhancing segmentation precision and reducing interference from surrounding tissue. The model's performance is quantitatively evaluated using the Dice similarity coefficient and HD95, demonstrating improved delineation of complex glioma structured. This approach offers a promising advancement in automating pediatric glioma segmentation, with the potential to improve clinical decision making and outcomes.