Intra-Patient Anatomic Statistical Models for Adaptive Radiotherapy

A statistical issue of clinical importance is intra-patient variation from day to day. We use these probability densities for segmentation of daily images by posterior optimization of deformable models. However, the information on intra- patient variation is only available after the multiple days of imaging; yet the densities are needed for segmentation on each day. Still, each patient's anatomy and image properties are distinct. We describe an approach of using sample means over the days so far to describe a Frechet mean of the patient. We assume intra-patient variation is stationary across patients, so one can pool training statistics on residues from the mean of the respective patient. The approach is applied both to principal geodesic analysis of m-rep residues describing anatomic variation and to PCA of intensity quantile residues from model-relative regions. In trials to date, application of these statistics in segmentations of male pelvic organs from CT in adaptive radiotherapy yields results competitive with human segmentations and with segmentations based fully on intra-patient statistics.