Federated Simulation for Medical Imaging

Daiqing Li1
Amlan Kar1,2,5
Nishant Ravikumar3,6

Alejandro F Frangi3,4,6
Sanja Fidler1,2,5

2University of Toronto
3University of Leeds
4KU Leuven
5Vector Institute

MICCAI 2020 (Early Accept)

Labelling data is expensive and time consuming especially for domains such as medical imaging that contain volumetric imaging data and require expert knowledge. Exploiting a larger pool of labeled data available across multiple centers, such as in federated learning, has also seen limited success since current deep learning approaches do not generalize well to images acquired with scanners from different manufacturers. We aim to address these problems in a common, learning-based image simulation framework which we refer to as Federated Simulation. We introduce a physics-driven generative approach that consists of two learnable neural modules: 1) a module that synthesizes 3D cardiac shapes along with their materials, and 2) a CT simulator that renders these into realistic 3D CT Volumes, with annotations. Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.



Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F Frangi, Sanja Fidler

Federated Simulation for Medical Imaging

MICCAI, 2020. (Early Accept)



Interactive Simulation Demo
Here we showcase an interactive simulation application of our approach using NVIDIA Omniverse Kit. The users can adjust the shape parameters of a cardiac mesh template and synthesize a novel (labeled) cardiac CT volume. Users can view different slices along the z axis by adjusting the slice bar. Notice that the generated volume and the input mesh are consistent across slices.
Presentation video at MICCAI 2020.

Federated Simulation

Medical data usually lives at multiple sites, and sharing is a problem due to patient privacy. In our work, we learn to synthesize labeled CT volumes in a federated setting that is privacy preserving. In the FL setup, we assume there is a central server that aggregates and maintains a global model for the organ shape and material across sites, while each individual site has a local simulator with specific device parameters and its own conditional GAN. The central server shares the global knowledge of shape and material parameters across sites, which they use to train their enhancer using local data. This setting only requires transmitting gradients with respect to the shape and material model parameters to learn a global simulator, which we hypothesize should contain minimal information about individual patients. To train models for downstream tasks, every site can simulate their own data and train their own unique task model, as opposed to traditional FL where one model architecture and weights must be used and shared across sites and gradients for the model itself must be transmitted.


Quantitative Results of training a Unet-3D binary segmentation model on our generated data on three datasets. We see that data generated by our methods (with access to a small subset of training labels) outperforms baselines on both the single site and federated simulation case. It sometimes outperforms the upper bound of using the full training set as well. Method with highest mean is in bold.

Qualitative Results: First two columns show random samples (full volume) from our full model on each of the datasets. Last two columns show nearest neighbour from the training set. We see that our model can generate plausible yet novel data samples with annotations (second column).