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Increased organ at risk segmentation accuracy is required to reduce cost andcomplications for patients receiving radiotherapy treatment. Some deep learningmethods for the segmentation of organs at risk use a two stage process where alocalisation network first crops an image to the relevant region and then alocally specialised network segments the cropped organ of interest. Weinvestigate the accuracy improvements brought about by such a localisationstage by comparing to a single-stage baseline network trained on fullresolution images. We find that localisation approaches can improve bothtraining time and stability and a two stage process involving both alocalisation and organ segmentation network provides a significant increase insegmentation accuracy for the spleen, pancreas and heart from the MedicalSegmentation Decathlon dataset. We also observe increased benefits oflocalisation for smaller organs. Source code that recreates the main results isavailable at \href{https://github.com/Abe404/localise_to_segment}{this httpsURL}.

Localise to segment: crop to improve organ at risk segmentation accuracy