The MICCAI federated tumor segmentation (FeTS) challenge 2024: efficient and robust aggregation methods

We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, focusing on federated learning (FL) for glioma sub-region segmentation in multi-parametric MRI scans, and evaluating novel weight
aggregation methods for increased robustness and efficiency. Participating methods from six teams are evaluated using a standardized FL setup and a multi-institutional dataset derived from the BraTS glioma benchmark—a dataset consisting of 1,251 training cases, 219 validation cases, and 570 hidden test cases, with segmentations of enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Teams are ranked by a cumulative scoring system accounting for segmentation performance—measured by Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95)—and communication efficiency assessed through the convergence score. A PID-controller-based approach emerges as the top-performing method, achieving a mean DSC of 0.733, 0.761, and 0.751 for ET, TC, and WT, respectively, with corresponding HD95 values of 33.922mm, 33.623mm, and 32.309mm, while also being the most efficient with a
convergence score of 0.764. These results contribute to ongoing advances in FL, building on top-performers from previous challenge iterations and surpassing them, highlighting PID controllers as powerful mechanisms for stabilizing and
optimizing weight aggregation in FL. The challenge code is available at https://github.com/FeTS-AI/Challenge