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Lung SBRT patients could have daily setup variations that lead to suboptimal treatment delivery. While currently a cone- beam CT (CBCT) is captured prior to each fraction for patient alignment, no organ contours or dosimetric calculations are routinely done to verify radiation therapy (RT) treatment delivery quality. Organ contours on CBCT are challenging because of the inferior image quality and low image contrast of CBCT. Besides, manual contouring is labor-intensive that prohibits clinical implementation. Therefore, if organ contours could be obtained automatically on fractional CBCT with limited human interventions, it would pave the way to obtain dosimetry for coverage evaluation and toxicity assessment. In this study, we developed a deep learning-based method for automated segmentation of multiple organs on CBCT images which simultaneously performs detection, classification, and segmentation of multi-organ. Our proposed hierarchical network method consists of four subnetworks: feature extractor, detector, hierarchical block, and mask module. The feature extractor subnetwork is used to extract informative features from CBCT. The detector subnetwork is used to locate the volume-of-interest (VOIs) of multiple organs. The hierarchical block network is used to enhance the feature contrast around organ boundaries and improve the organ classification. The mask module subnetwork then segments organ from the refined feature map within the VOIs. We conducted a five-fold cross-validation on 30 CBCTs. Five organs (esophagus, heart, spinal cord, left lung, and right lung) were segmented and compared with manual contours using several evaluation metrics. The Dice similarity coefficient (DSC) is 0.68, 0.87, 0.80, 0.91 and 0.93 for esophagus, heart, spinal cord, left lung, and right lung, respectively. These results demonstrate the feasibility and efficacy of our proposed hierarchical network method for CBCT lung segmentation, which could be used for fractional delivery dose evaluations in the future.
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