  Abdominal organ segmentation is a difficult and time-consuming task. To
reduce the burden on clinical experts, fully-automated methods are highly
desirable. Current approaches are dominated by Convolutional Neural Networks
(CNNs) however the computational requirements and the need for large data sets
limit their application in practice. By implementing a small and efficient
custom 3D CNN, compiling the trained model and optimizing the computational
graph: our approach produces high accuracy segmentations (Dice Similarity
Coefficient (%): Liver: 97.3$\pm$1.3, Kidneys: 94.8$\pm$3.6, Spleen:
96.4$\pm$3.0, Pancreas: 80.9$\pm$10.1) at a rate of 1.6 seconds per image.
Crucially, we are able to perform segmentation inference solely on CPU (no GPU
required), thereby facilitating easy and widespread deployment of the model
without specialist hardware.
