  We compare two conditioning mechanisms based on concatenation and
feature-wise modulation to integrate non-imaging information into convolutional
neural networks for segmentation of anatomical structures. As a
proof-of-concept we provide the distribution of class labels obtained from
ground truth masks to ensure strong correlation between the conditioning data
and the segmentation maps. We evaluate the methods on the ACDC dataset, and
show that conditioning with non-imaging data improves performance of the
segmentation networks. We observed conditioning the U-Net architectures was
challenging, where no method gave significant improvement. However, the same
architecture without skip connections outperforms the baseline with
feature-wise modulation, and the relative performance increases as the training
size decreases.
