Federated and Centralized Machine Learning for Cell Segmentation: A Comparative Analysis

The automatic segmentation of cell images plays a critical role in medicine and biology, as it enables faster and more accurate analysis and diagnosis. Traditional machine learning faces challenges since it requires transferring sensitive data from laboratories to the cloud, with possible risks and limitations due to patients' privacy, data-sharing regulations, or laboratory privacy guidelines. Federated learning addresses data-sharing issues by introducing a decentralized approach that removes the need for laboratories' data sharing. The learning task is divided among the participating clients, with each training a global model situated on the cloud with its local dataset. This guarantees privacy by only transmitting updated model weights to the cloud. In this study, the centralized learning approach for cell segmentation is compared with the federated one, demonstrating that they achieve similar performances. Stemming from a benchmarking of available cell segmentation models, Cellpose, having shown better recall and precision (F1=0.84) than U-Net (F1=0.50) and StarDist (F1=0.12), was used as the baseline for a federated learning testbench implementation. The results show that both binary segmentation and multi-class segmentation metrics remain high when employing both the centralized solution (F1=0.86) and the federated solution (F12clients=0.86). These results were also stable across an increasing number of clients and a reduced number of local data samples (F14clients=0.87, F116clients=0.86), proving the effectiveness of central aggregation on the cloud of locally trained models.