Automated delineation of head and neck organs at risk using synthetic MRI‐aided mask scoring regional convolutional neural network

• Published in: Medical physics (Lancaster) Vol. 48; no. 10; pp. 5862 - 5873
• Main Authors: Dai, Xianjin, Lei, Yang, Wang, Tonghe, Zhou, Jun, Roper, Justin, McDonald, Mark, Beitler, Jonathan J., Curran, Walter J., Liu, Tian, Yang, Xiaofeng
• Format: Journal Article
• Language: English
• Published: United States 01.10.2021
• Subjects: deep learning; Head - diagnostic imaging; Head and Neck Neoplasms - diagnostic imaging; Head and Neck Neoplasms - radiotherapy; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; multi‐organ segmentation; Neural Networks, Computer; Organs at Risk; Radiotherapy Planning, Computer-Assisted; synthetic MRI; synthetic MRI; deep learning; multi-organ segmentation
• ISSN: 0094-2405; 2473-4209; 1522-8541; 2473-4209
• DOI: 10.1002/mp.15146

Purpose Auto‐segmentation algorithms offer a potential solution to eliminate the labor‐intensive, time‐consuming, and observer‐dependent manual delineation of organs‐at‐risk (OARs) in radiotherapy treatment planning. This study aimed to develop a deep learning‐based automated OAR delineation method to tackle the current challenges remaining in achieving reliable expert performance with the state‐of‐the‐art auto‐delineation algorithms. Methods The accuracy of OAR delineation is expected to be improved by utilizing the complementary contrasts provided by computed tomography (CT) (bony‐structure contrast) and magnetic resonance imaging (MRI) (soft‐tissue contrast). Given CT images, synthetic MR images were firstly generated by a pre‐trained cycle‐consistent generative adversarial network. The features of CT and synthetic MRI were then extracted and combined for the final delineation of organs using mask scoring regional convolutional neural network. Both in‐house and public datasets containing CT scans from head‐and‐neck (HN) cancer patients were adopted to quantitatively evaluate the performance of the proposed method against current state‐of‐the‐art algorithms in metrics including Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and residual mean square distance (RMS). Results Across all of 18 OARs in our in‐house dataset, the proposed method achieved an average DSC, HD95, MSD, and RMS of 0.77 (0.58–0.90), 2.90 mm (1.32–7.63 mm), 0.89 mm (0.42–1.85 mm), and 1.44 mm (0.71–3.15 mm), respectively, outperforming the current state‐of‐the‐art algorithms by 6%, 16%, 25%, and 36%, respectively. On public datasets, for all nine OARs, an average DSC of 0.86 (0.73–0.97) were achieved, 6% better than the competing methods. Conclusion We demonstrated the feasibility of a synthetic MRI‐aided deep learning framework for automated delineation of OARs in HN radiotherapy treatment planning. The proposed method could be adopted into routine HN cancer radiotherapy treatment planning to rapidly contour OARs with high accuracy.