Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy

Background Using radiation therapy RT to treat head and neck H N cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment however patient motion can still occur Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects Tracking tumor motion would enable motion compensation during RT leading to more accurate dose delivery Purpose The purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT Unlike previous tumor segmentation methods for kV images in this paper a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment Method In this paper a conditional generative adversarial network cGAN is presented that can detect and segment the gross tumor volume GTV in kV images acquired during H N RT Retrospective data from 15 H N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient specific cGANs The training data consisted of digitally reconstructed radiographs DRRs generated from each patient s planning CT and contoured GTV Training data was augmented by using synthetically deformed CTs to generate additional DRRs in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients containing realistic patient motion The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion The testing dataset consisted of 1080 DRRs for each patient obtained by deforming the planning CT and GTV at different magnitudes to the training data The accuracy of the generated segmentations was evalu