Purpose or Objective Segmenting organs in Cone-Beam CT (CBCT) images would allow to adapt the dose delivered based on the organ deformations that occured between the treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deep-learning based automatic segmentation approaches have shown impressive successes and may be of interest here but required to train a convolutional neural network (CNN) from a database of segmented CBCT images, which can be difficult to obtain. In this work, we propose to train a CNN from a database of artificial CBCT images simulated from planning CT for which it is easier to obtain the organ delineations.