Segmentation of prostate from CT scans using a combined voxel random forests classification with spherical harmonics regularization

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.