In prostate cancer external beam radiotherapy, pelvic structures identification in computed tomography (CT) is required for the treatment planning and is performed manually by experts. Prostate manual delineations in CT modality is time consuming and prone to observer variability. We propose a fully automated process using a combination of a Random Forests (RF) classification and Spherical Harmonics (SPHARM) to identify the prostate boundaries. The proposed method outperformed classical atlas based approach from the literature. Combining RF to detect the prostate and SPHARM for shape regularization provided promising results for automatic prostate segmentation.
Nowadays, the de nition of patient-speci c constraints in prostate cancer radiotherapy planning are solely based on dose-volume histogram (DVH) parameters. Nevertheless those DVH models lack of spatial accuracy since they do not use the complete 3D information of the dose distribution. The goal of the study was to propose an automatic work ow to de ne patient-speci c rectal sub-regions (RSR) involved in rectal bleeding (RB) in case of prostate cancer radiotherapy. A multi-atlas database spanning the large rectal shape variability was built from a population of 116 individuals. Non-rigid registration followed by voxel-wise statistical analysis on those templates allowed nding RSR likely correlated with RB (from a learning cohort of 63 patients). To de ne patient-speci c RSR, weighted atlas-based segmentation with a vote was then applied to 30 test patients. Results show the potentiality of the method to be used for patient-speci c planning of intensity modulated radiotherapy (IMRT).