Pelvic multi‐organ segmentation on cone‐beam CT for prostate adaptive radiotherapy

• Published in: Medical physics (Lancaster) 2020-08, Vol.47 (8), p.3415-3422
• Main Authors: Fu, Yabo, Lei, Yang, Wang, Tonghe, Tian, Sibo, Patel, Pretesh, Jani, Ashesh B., Curran, Walter J., Liu, Tian, Yang, Xiaofeng
• Format: Article
• Language: English
• Subjects: adaptive radiotherapy; Cone-Beam Computed Tomography; cone‐beam CT; deep learning; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; multi‐organ segmentation; Organs at Risk; Pelvis - diagnostic imaging; Prostate - diagnostic imaging; Radiotherapy Planning, Computer-Assisted; synthetic MRI
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.14196

Background and purpose The purpose of this study is to develop a deep learning‐based approach to simultaneously segment five pelvic organs including prostate, bladder, rectum, left and right femoral heads on cone‐beam CT (CBCT), as required elements for prostate adaptive radiotherapy planning. Materials and methods We propose to utilize both CBCT and CBCT‐based synthetic MRI (sMRI) for the segmentation of soft tissue and bony structures, as they provide complementary information for pelvic organ segmentation. CBCT images have superior bony structure contrast and sMRIs have superior soft tissue contrast. Prior to segmentation, sMRI was generated using a cycle‐consistent adversarial networks (CycleGAN), which was trained using paired CBCT‐MR images. To combine the advantages of both CBCT and sMRI, we developed a cross‐modality attention pyramid network with late feature fusion. Our method processes CBCT and sMRI inputs separately to extract CBCT‐specific and sMRI‐specific features prior to combining them in a late‐fusion network for final segmentation. The network was trained and tested using 100 patients’ datasets, with each dataset including the CBCT and manual physician contours. For comparison, we trained another two networks with different network inputs and architectures. The segmentation results were compared to manual contours for evaluations. Results For the proposed method, dice similarity coefficients and mean surface distances between the segmentation results and the ground truth were 0.96 ± 0.03, 0.65 ± 0.67 mm; 0.91 ± 0.08, 0.93 ± 0.96 mm; 0.93 ± 0.04, 0.72 ± 0.61 mm; 0.95 ± 0.05, 1.05 ± 1.40 mm; and 0.95 ± 0.05, 1.08 ± 1.48 mm for bladder, prostate, rectum, left and right femoral heads, respectively. As compared to the other two competing methods, our method has shown superior performance in terms of the segmentation accuracy. Conclusion We developed a deep learning‐based segmentation method to rapidly and accurately segment five pelvic organs simultaneously from daily CBCTs. The proposed method could be used in the clinic to support rapid target and organs‐at‐risk contouring for prostate adaptive radiation therapy.