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In Physics in medicine and biology

Organ-at-risk (OAR) delineation is a key step for cone-beam CT (CBCT) based adaptive radiotherapy planning that can be a time-consuming, labor-intensive, and subject-to-variability process. We aim to develop a fully automated approach aided by synthetic MRI for rapid and accurate CBCT multi-organ contouring in head-and-neck (HN) cancer patients. MRI has superb soft-tissue contrasts, while CBCT offers bony-structure contrasts. Using the complementary information provided by MRI and CBCT is expected to enable accurate multi-organ segmentation in HN cancer patients. In our proposed method, MR images are firstly synthesized using a pre-trained cycle-consistent generative adversarial network given CBCT. The features of CBCT and synthetic MRI are then extracted using dual pyramid networks for final delineation of organs. CBCT images and their corresponding manual contours were used as pairs to train and test the proposed model. Quantitative metrics including Dice similarity coefficient (DSC), Hausdorff distance 95% (HD95), mean surface distance (MSD), and residual mean square distance (RMS) were used to evaluate the proposed method. The proposed method was evaluated on a cohort of 65 HN cancer patients. CBCT images were collected from those patients who received proton therapy. Overall, DSC values of 0.87±0.03, 0.79±0.10/0.79±0.11, 0.89±0.08/0.89±0.07, 0.90±0.08, 0.75±0.06/0.77±0.06, 0.86±0.13, 0.66±0.14, 0.78±0.05/0.77±0.04, 0.96±0.04, 0.89±0.04/0.89±0.04, 0.83±0.02, and 0.84±0.07 for commonly used OARs for treatment planning including brain stem, left/right cochlea, left/right eye, larynx, left/right lens, mandible, optic chiasm, left/right optic nerve, oral cavity, left/right parotid, pharynx, and spinal cord, respectively, were achieved. In this study, we developed a synthetic MRI-aided HN CBCT auto-segmentation method based on deep learning. It provides a rapid and accurate OAR auto-delineation approach, which can be used for adaptive radiation therapy.

Dai Xianjin, Lei Yang, Wang Tonghe, Dhabaan Anees, McDonald Mark, Beitler Jonathan J, Curran Walter J, Zhou Jun, Liu Tian, Yang Xiaofeng


CBCT, Deep Learning, Head and Neck, Organ Segmentation