Automated CT Segmentation for Rapid Assessment of Anatomical Variations in Head-and-Neck Radiation Therapy

Typical radiation therapy for head-and-neck cancer patients lasts for more than a month. Anatomical variations often occur along the treatment course due to the tumor shrinkage and weight loss, particularly for head and neck (HN) cancer patients. To maintain the accuracy of radiotherapy beam delivery, weekly quality assurance (QA) CT is sometime acquired to monitor patients' anatomical changes, and re-plan the treatment if needed. However, the re-plan is a labor-intensive and time-consuming process, thus, decisions of re-plan are always made cautiously. In this study, we aim to develop a deep learning-based method for automated segmentation of multi-organ from CT head and neck (HN) images to rapidly evaluate the anatomical variations. Our proposed method, named detecting and boosting network, consists of one pre-trained fully convolutional one stage objection detector (FCOS) and two learnable subnetworks, i.e., hierarchical block and mask head. FCOS is used to extract informative features from CT and locate the volume-of-interest (VOIs) of multiple organs. Hierarchical block is used to enhance the feature contrast around organ boundary and thus improve the ability of organ classification. Mask head then segment organ from the refined feature map within the VOIs. We conducted a five-fold cross-validation on 35 patients' cases who have multiple weekly CT scans (over 100 QACTs) during their radiotherapy. The 11 organs were segmented and compared with manual contours using several segmentation measurements. The mean Dice similarity coefficient (DSC) values of 0.82, 0.82, and 0.81 were achieved along the treatment course for all the organs. These results demonstrate the feasibility and efficacy of our proposed method for multi-OAR segmentation from HN CT, which can be used for rapid evaluate the anatomical variations in HN radiation therapy.