Federated Semi-Supervised Image Segmentation with Dynamic Client Selection

ObjectiveMulticenter validation is a growing requirement in clinical research, yet strict privacy regulations, heterogeneous cross-institutional data distributions, and scarce pixel-level annotations limit the use of conventional centralized medical image segmentation models. This study develops a federated semi-supervised framework that uses labeled and unlabeled prostate MRI data from multiple hospitals, considers dynamic client participation and Non-Independent and Identically Distributed (Non-IID) data, and aims to improve segmentation accuracy and robustness under real-world constraints.MethodsA cross-silo Federated Semi-Supervised Learning (FSSL) paradigm is used. Clients with pixel-wise annotations act as labeled clients, and those without annotations act as unlabeled clients. Each client maintains a local student network for prostate segmentation. On unlabeled clients, a teacher network with the same architecture is updated using the exponential moving average of student parameters and generates perturbed pseudo-labels to supervise the student through a hybrid consistency loss that combines Dice and binary cross-entropy terms. To reduce the effect of heterogeneous and low-quality updates, a performance-driven dynamic client selection and aggregation strategy is applied. At each communication round, clients are evaluated on their local validation sets, and only those whose Dice scores exceed a threshold are retained. A top-K subset is then aggregated with normalized contribution weights derived from validation Dice, with bounds to avoid gradient vanishing and single-client dominance. For unlabeled clients, a penalty factor down-weights unreliable pseudo-labeled updates. The segmentation backbone is a Multi-scale Feature Fusion U-Net (MFF-UNet). Starting from a standard encoder–decoder U-Net, an FPN-like pyramid is added to the encoder, where multi-level feature maps are channel-aligned using 1$* $1 convolutions, fused in a top-down pathway through upsampling and element-wise addition, and refined using 3$* $3 convolutions. The decoder upsamples these fused features and combines them with encoder features through skip connections, enabling joint modeling of global semantics and fine-grained boundaries. The framework is evaluated on T2-weighted prostate MRI from six centers, comprising three labeled and three unlabeled clients. All 3D volumes are resampled, sliced into 2D axial images, resized, and augmented. The Dice coefficient and 95th percentile Hausdorff distance (HD95) are used as evaluation metrics.Results and DiscussionsOn the six-center dataset, the method achieves average Dice scores of 0.840 5 on labeled clients and 0.786 8 on unlabeled clients, with corresponding HD95 values of 8.04 and 8.67 pixel. These results are superior to or comparable with several representative federated semi-supervised or mixed-supervision methods, with the largest gains on distribution-shifted unlabeled centers. Visualization shows that the method generates more complete and smoother prostate contours with fewer false positives in low-contrast or small-volume cases than the baselines. Attention heatmaps from the final decoder layer indicate that UNet exhibits attention drift, SegMamba produces diffuse responses, and nnU-Net shows weak activations for small lesions, whereas MFF-UNet focuses more precisely on the prostate region with stable high responses, indicating improved discriminative capability and interpretability.ConclusionsA federated semi-supervised prostate MRI segmentation framework that integrates teacher-student consistency learning, multi-scale feature fusion, and performance-driven dynamic client selection is presented. The method preserves privacy by keeping data local, reduces annotation scarcity by using unlabeled clients, and addresses client heterogeneity through reliability-aware aggregation. Experiments on a six-center dataset show that the framework achieves competitive or superior overlap and boundary accuracy compared with state-of-the-art federated semi-supervised methods, particularly on distribution-shifted unlabeled centers. The framework is model-agnostic and can be applied to other organs, imaging modalities, and cross-institutional segmentation tasks under strict privacy and regulatory constraints.