Local jet features and statistical models in a hybrid Bayesian framework for prostate estimation in CBCT images.

International audienceThe challenge in prostate cancer radiotherapy is to deliver the planned dose to the prostate, sparing as much as possible the neighboring organs, namely bladder and rectum. If a lower amount of dose, compared to the prescription, is delivered to the prostate, the risk of failure may increase. Likewise, if higher doses are delivered to the neighboring organs, undesirable side effects may occur. Accurate localization of prostate and organs at risk is therefore a bottleneck in radiotherapy. In recent Image Guided Radiotherapy (IGRT) procedures, an intra-operative Cone Beam CT (CBCT) is used at each session to align the prostate to the planned CT and to maximize the correct dose delivery. Tracking the prostate in these images may allow not only to achieve this goal but also to accurately measure the cumulated dose as the session goes. This work introduces a new method that automatically locates the prostate in CBCT images. The whole method lies in a Bayesian formulation where a multiscale image representation, the local jets, is used as a likelihood function, an the prior knowledge is learned from multiple examples by expert manual delineations. Compared with manual ground truth segmentations, the results showed a Jaccard similarity index of 0.84, and an accuracy of 98%in a set of 4 studies of four patients