  Even though many semantic segmentation methods exist that are able to perform
well on many medical datasets, often, they are not designed for direct use in
clinical practice. The two main concerns are generalization to unseen data with
a different visual appearance, e.g., images acquired using a different scanner,
and efficiency in terms of computation time and required Graphics Processing
Unit (GPU) memory. In this work, we employ a multi-organ segmentation model
based on the SpatialConfiguration-Net (SCN), which integrates prior knowledge
of the spatial configuration among the labelled organs to resolve spurious
responses in the network outputs. Furthermore, we modified the architecture of
the segmentation model to reduce its memory footprint as much as possible
without drastically impacting the quality of the predictions. Lastly, we
implemented a minimal inference script for which we optimized both, execution
time and required GPU memory.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning