Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is still a challenge due to the poor soft tissue contrast, artifacts, and limited field-of-view of these images, alongside the lack of large, annotated datasets to train deep learning models. This study aims to develop a comprehensive framework to segment 25 HN OARs on CBCT to facilitate treatment replanning.Approach. The proposed framework was developed in three steps: (i) refining an in-house framework to segment 25 OARs on computed tomography (CT)
(ii) training a deep learning model to segment the same OARs on synthetic CT (sCT) images derived from CBCT using contours propagated from CT as ground truth, integrating high-contrast information from CT and texture features of sCT
and (iii) validating the clinical relevance of sCT segmentations through a dosimetric analysis on an external cohort. Main results. Most OARs achieved a Dice Score Coefficient over 70%, with mean Average Surface Distances of 1.30 mm for CT and 1.27 mm for sCT. The dosimetric analysis demonstrated a strong agreement in the mean dose and D2 (%) values, with most OARs showing non-significant differences between automatic CT and sCT segmentations. Significance. These results support the feasibility and clinical relevance of using deep learning models for OAR segmentation on both CT and CBCT for HNC RT.
.