  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.
