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Enhancing efficiency in pediatric brain tumor segmentation using a pathologically diverse single-center clinical
Arianna Piffer1, Josef Alois Buchner2, Antonio Giulio Gennari3,4
1Division of Oncology and Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.
Neuro-Oncology Advances
|March 27, 2026
Summary
Deep learning segmentation accurately delineates whole pediatric brain tumors (PBTs) and T2-hyperintensity, comparable to human experts. Further refinement is needed for enhancing tumor and cystic component segmentation in PBTs.
Area of Science:
- Medical imaging analysis
- Artificial intelligence in oncology
Background:
- Pediatric brain tumors (PBTs) are diverse and challenging to diagnose and treat.
- Deep learning (DL) shows promise for PBT segmentation but requires validation across subtypes and MRI protocols.
Purpose of the Study:
- To evaluate the performance of a 3D nnU-Net model for segmenting various pediatric brain tumor subregions.
- To assess DL segmentation accuracy across different PBT subtypes, MRI sequences, and tumor locations.
Main Methods:
- Retrospective analysis of 174 pediatric patients with diverse brain tumors.
- Utilized MRI sequences (T1, T1-C, T2, FLAIR) with manual annotations for whole tumor (WT), T2-hyperintensity (T2H), enhancing tumor (ET), and cystic component (CC).
- Trained and tested a 3D nnU-Net model, evaluating performance using Dice Similarity Coefficient (DSC) against human variability.
Main Results:
- The DL model achieved robust performance for WT and T2H segmentation (mean DSC: 0.85), comparable to human annotators.
- Moderate accuracy was observed for ET segmentation (mean DSC: 0.75), with poor performance for CC (mean DSC: 0.26).
- Segmentation accuracy varied by tumor type, MRI sequence combination, and location; T1, T1-C, and T2 sequences yielded results similar to the full protocol.
Conclusions:
- Deep learning-based segmentation is feasible and effective for WT and T2H in PBTs.
- Challenges persist in segmenting ET and CC, necessitating further model development.
- Findings suggest potential for protocol simplification and automated volumetric assessment in pediatric neuro-oncology.
