Segmentation of head-and-neck organs-at-risk in longitudinal CT scans combining deformable registrations and convolutional neural networks

ABSTRACT Automated segmentation of organs-at-risk (OAR) in follow-up images of the patient acquired during the course of treatment could greatly facilitate adaptive treatment planning in radiotherapy. Instead of segmenting each image separately, the segmentation could be improved by making use of the additional information provided by longitudinal data of previously segmented images of the same patient. We propose a tool for automated segmentation of longitudinal data that combines deformable image registration (DIR) and convolutional neural networks (CNN). The segmentation propagated by DIR from a previous image onto the current image and the segmentation obtained by a separately trained cross-sectional CNN applied to the current image are given as input to a longitudinal CNN, together with the images itself, that is trained to optimally predict the manual ground truth segmentation using all available information. Despite the fairly limited amount of training data available in this study, an improvement of the segmentation of five different OAR in head-and-neck CT scans was found compared to both the results of DIR and the cross-sectional CNN separately in terms of Dice coefficient.