Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net

Accurate segmentation of organs at risk (OARs) plays a critical role in the treatment planning of image-guided radiotherapy of head and neck cancer. This segmentation task is challenging for both humans and automated algorithms because of the relatively large number of OARs to be segmented, the large variability in size and morphology across different OARs, and the low contrast between some OARs and the background. In this study, we propose a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two subtasks: locating a bounding box of the OAR and segmenting the OAR from a small volume within the bounding box, and each subtask is fulfilled by a dedicated 3D U-Net. The decomposition makes each subtask much easier so that it can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods using the Medical Image Computing and Computer-Assisted Intervention 2015 Challenge dataset. In terms of the boundary-based metric 95% Hausdorff distance, the proposed method ranked first for seven of nine OARs and ranked second for the other OARs. In terms of the area-based metric dice similarity coefficient, the proposed method ranked first for five of nine OARs and ranked second for the other three OARs with a small difference from the method that ranked first.