  Given the high incidence and effective treatment options for liver diseases,
they are of great socioeconomic importance. One of the most common methods for
analyzing CT and MRI images for diagnosis and follow-up treatment is liver
segmentation. Recent advances in deep learning have demonstrated encouraging
results for automatic liver segmentation. Despite this, their success depends
primarily on the availability of an annotated database, which is often not
available because of privacy concerns. Federated Learning has been recently
proposed as a solution to alleviate these challenges by training a shared
global model on distributed clients without access to their local databases.
Nevertheless, Federated Learning does not perform well when it is trained on a
high degree of heterogeneity of image data due to multi-modal imaging, such as
CT and MRI, and multiple scanner types. To this end, we propose Fednorm and its
extension \fednormp, two Federated Learning algorithms that use a
modality-based normalization technique. Specifically, Fednorm normalizes the
features on a client-level, while Fednorm+ employs the modality information of
single slices in the feature normalization. Our methods were validated using
428 patients from six publicly available databases and compared to
state-of-the-art Federated Learning algorithms and baseline models in
heterogeneous settings (multi-institutional, multi-modal data). The
experimental results demonstrate that our methods show an overall acceptable
performance, achieve Dice per patient scores up to 0.961, consistently
outperform locally trained models, and are on par or slightly better than
centralized models.
