Overcoming Data Scarcity through Multi-Center Federated Learning for Organs-at-Risk Segmentation in Pediatric Upper Abdominal Radiotherapy
Recent advancements in deep learning have revolutionized the field of medical imaging, particularly in enhancing the workflow of radiotherapy through automated contouring of organs and structures at risk (OARs). However, models trained predominantly on adult data have demonstrated inadequate performance when applied to pediatric patients. This discrepancy arises from the challenges of data scarcity and fragmentation across different medical centers. In an innovative approach, researchers are leveraging federated learning (FL) to facilitate privacy-preserving collaborative training, thus circumventing the need for data sharing among institutions.
Study Overview
A recent study, documented in arXiv:2605.06820v1, explored the feasibility and efficacy of employing federated learning to develop pediatric-specific OAR segmentation models. The research was conducted across two prominent European medical centers, Utrecht and Heidelberg, focusing on pediatric patients diagnosed with renal tumors or abdominal neuroblastoma.
Methodology
The study utilized retrospective data collection of computed tomography (CT) images from pediatric patients, which were processed locally at each center. An nnU-Net-based framework was employed to segment 19 OARs using both local and federated learning methodologies. Key features of the methodology included:
- Secure weight exchange implemented on a cloud storage system across institutional firewalls.
- Performance assessment based on several metrics, including the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance, and mean surface distance.
- Robustness evaluation against variations in patient orientation and false-positive segmentations of surgically removed kidneys.
Results
The study incorporated a total of 310 postoperative CT scans from 272 pediatric patients, with 105 cases of renal tumors and 167 cases of neuroblastoma. The results revealed significant insights:
- Local models exhibited commendable performance on their respective center data; however, cross-center performance was notably subpar for four to seven out of the nine evaluated OARs.
- In contrast, the federated learning model demonstrated comparable performance to local models for at least seven out of nine OARs and excelled in cross-center evaluations across all three metrics.
- The FL model achieved an impressive increase in DSC, with gains ranging from 0.003 to 0.007 over local models.
- Furthermore, federated learning maintained consistent performance across differing patient orientations and effectively reduced false-positive segmentations of kidneys.
Conclusion
The findings underscore the potential of real-world federated learning to enhance the robustness of CT-based OAR segmentation models specifically tailored for pediatric upper abdominal tumors. By enabling multi-center collaboration while preserving patient privacy, federated learning emerges as a pivotal strategy for overcoming data scarcity and fragmentation challenges in the medical imaging landscape. This innovative approach not only paves the way for improved patient outcomes but also sets a precedent for future research and development in pediatric radiotherapy.
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