  Increased organ at risk segmentation accuracy is required to reduce cost and
complications for patients receiving radiotherapy treatment. Some deep learning
methods for the segmentation of organs at risk use a two stage process where a
localisation network first crops an image to the relevant region and then a
locally specialised network segments the cropped organ of interest. We
investigate the accuracy improvements brought about by such a localisation
stage by comparing to a single-stage baseline network trained on full
resolution images. We find that localisation approaches can improve both
training time and stability and a two stage process involving both a
localisation and organ segmentation network provides a significant increase in
segmentation accuracy for the spleen, pancreas and heart from the Medical
Segmentation Decathlon dataset. We also observe increased benefits of
localisation for smaller organs. Source code that recreates the main results is
available at \href{https://github.com/Abe404/localise_to_segment}{this https
URL}.
