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In Physics in medicine and biology ; h5-index 0.0

PURPOSE : To develop an automated cone-beam computed tomography (CBCT) multi-organ segmentation method for potential CBCT-guided adaptive radiation therapy workflow.

METHODS AND MATERIALS : The proposed method combines the deep-leaning-based image synthesis method, which generates magnetic resonance images (MRIs) with superior soft-tissue contrast from on-board setup CBCT images to aid CBCT segmentation, with a deep attention strategy, which focuses on learning discriminative features for differentiating organ margins. The whole segmentation method consists of 3 major steps. First, a cycle-consistent adversarial network (CycleGAN) was used to estimate a synthetic MRI (sMRI) from CBCT images. Second, a deep attention network was trained based on sMRI and the manual contours deformed from MRIs. Third, the segmented contours for a query patient was obtained by feeding the patient's CBCT images into the trained sMRI estimation and segmentation model. In our retrospective study, we included 100 prostate cancer patients, each of whom has CBCT acquired with prostate, bladder and rectum contoured by physicians as ground truth. We trained and tested our model with separate datasets among these patients. The resulting segmentations were compared with physicians' manual contours.

RESULTS : The Dice similarity coefficient and mean surface distance indices between our segmented and physicians' manual contours (bladder, prostate, and rectum) were 0.95±0.02, 0.44±0.22 mm, 0.86±0.06, 0.73±0.37 mm, and 0.91±0.04, 0.72±0.65 mm, respectively.

CONCLUSION : We have proposed a novel CBCT-only prostate segmentation strategy using CBCT-based sMRI and validated its accuracy against pelvic multi-organ contours that were manually drawn on MR images and deformed to CT images. This technique could provide accurate organ volume for treatment planning without requiring MR images acquisition, greatly facilitating routine clinical workflow.

Lei Yang, Wang Tonghe, Tian Sibo, Dong Xue, Jani Ashesh B, Schuster David, Curran Walter J, Patel Pretesh, Liu Tian, Yang Xiaofeng


Adaptive Radiotherapy, CBCT, Deep Learning, Segmentation