Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network

• Authors: Xue Dong, Emory University; Yang Lei, Emory University; Sibo Tian, Emory University; Tonghe Wang, Emory University; Pretesh Patel, Emory University; Walter Curran Jr, Emory University; Ashesh Jani, Emory University; Tian Liu, Emory University; Xiaofeng Yang, Emory University
• Journal Title: RADIOTHERAPY AND ONCOLOGY
• Volume: Volume 141
• Publisher: ELSEVIER IRELAND LTD | 2019-12-01, Pages 192-199
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.radonc.2019.09.028
• Subjects: Health Sciences, Oncology; Health Sciences, Radiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Oncology; Radiology, Nuclear Medicine & Medical Imaging; Multi-organ segmentation; Deep learning; Synthetic MRI; VOLUME DELINEATION; AUTO-SEGMENTATION; PROSTATE; IMAGES; RADIOTHERAPY; HEAD

Abstract

Background and purpose: Manual contouring is labor intensive, and subject to variations in operator knowledge, experience and technique. This work aims to develop an automated computed tomography (CT) multi-organ segmentation method for prostate cancer treatment planning. Methods and materials: The proposed method exploits the superior soft-tissue information provided by synthetic MRI (sMRI) to aid the multi-organ segmentation on pelvic CT images. A cycle generative adversarial network (CycleGAN) was used to estimate sMRIs from CT images. A deep attention U-Net (DAUnet) was trained on sMRI and corresponding multi-organ contours for auto-segmentation. The deep attention strategy was introduced to identify the most relevant features to differentiate different organs. Deep supervision was incorporated into the DAUnet to enhance the features’ discriminative ability. Segmented contours of a patient were obtained by feeding CT image into the trained CycleGAN to generate sMRI, which was then fed to the trained DAUnet to generate organ contours. We trained and evaluated our model with 140 datasets from prostate patients. Results: The Dice similarity coefficient and mean surface distance between our segmented and bladder, prostate, and rectum manual contours were 0.95 ± 0.03, 0.52 ± 0.22 mm; 0.87 ± 0.04, 0.93 ± 0.51 mm; and 0.89 ± 0.04, 0.92 ± 1.03 mm, respectively. Conclusion: We proposed a sMRI-aided multi-organ automatic segmentation method on pelvic CT images. By integrating deep attention and deep supervision strategy, the proposed network provides accurate and consistent prostate, bladder and rectum segmentation, and has the potential to facilitate routine prostate-cancer radiotherapy treatment planning.