  Accurate segmentation of organ at risk (OAR) play a critical role in the
treatment planning of image guided radiation treatment of head and neck cancer.
This segmentation task is challenging for both human and automatic algorithms
because of the relatively large number of OARs to be segmented, the large
variability of the size and morphology across different OARs, and the low
contrast of between some OARs and the background. In this paper, we proposed a
two-stage segmentation framework based on 3D U-Net. In this framework, the
segmentation of each OAR is decomposed into two sub-tasks: locating a bounding
box of the OAR and segment it from a small volume within the bounding box, and
each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes
each of the two sub-tasks much easier, so that they can be better completed. We
evaluated the proposed method and compared it to state-of-the-art methods by
using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric
95HD, the proposed method ranked first in eight of all nine OARs and ranked
second in the other OAR. In terms of the area-based metric DSC, the proposed
method ranked first in six of the nine OARs and ranked second in the other
three OARs with small difference with the first one.
