  Objective: For real-time markerless tumour tracking in stereotactic lung radiotherapy, we propose a different approach which uses patient-specific deep learning (DL) using a personalized data generation strategy, avoiding the need for collection of a large patient data set. We validated our strategy with digital phantom simulation and epoxy phantom studies.   Methods: We developed lung tumour tracking for radiotherapy using a convolutional neural network trained for each phantom's lesion by using multiple digitally reconstructed radiographs (DRRs) generated from each phantom's treatment planning 4D-CT. We trained tumour-bone differentiation using large numbers of training DRRs generated with various projection geometries to simulate tumour motion. We solved the problem of using DRRs for training and X-ray images for tracking by using the training DRRs with random contrast transformation and random noise addition.   Results: We defined adequate tracking accuracy as the % frames satisfying < 1 mm tracking error of the isocentre. In the simulation study, we achieved 100% tracking accuracy in 3-cm spherical and 1.5 x 2.25 x 3-cm ovoid masses. In the phantom study, we achieved 100% and 94.7% tracking accuracy in 3- and 2-cm spherical masses, respectively. This required 32.5 ms/frame (30.8 fps) real-time processing.   Conclusions: We proved the potential feasibility of a real-time markerless tumour tracking framework for stereotactic lung radiotherapy based on patient-specific DL with personalized data generation with digital phantom and epoxy phantom studies.   Advances in Knowledge: Using DL with personalized data generation is an efficient strategy for real-time lung tumour tracking. 
Real-time markerless tumour tracking with patient-specific deep learning   using a personalized data generation strategy: Proof of concept by phantom   study