  The accurate segmentation of organs-at-risk (OARs) in head and neck CT images
is a critical step for radiation therapy of head and neck cancer patients.
However, manual delineation for numerous OARs is time-consuming and laborious,
even for expert oncologists. Moreover, manual delineation results are
susceptible to high intra- and inter-variability. To this end, we propose a
novel dual shape guided network (DSGnet) to automatically delineate nine
important OARs in head and neck CT images. To deal with the large shape
variation and unclear boundary of OARs in CT images, we represent the organ
shape using an organ-specific unilateral inverse-distance map (UIDM) and guide
the segmentation task from two different perspectives: direct shape guidance by
following the segmentation prediction and across shape guidance by sharing the
segmentation feature. In the direct shape guidance, the segmentation prediction
is not only supervised by the true label mask, but also by the true UIDM, which
is implemented through a simple yet effective encoder-decoder mapping from the
label space to the distance space. In the across shape guidance, UIDM is used
to facilitate the segmentation by optimizing the shared feature maps. For the
experiments, we build a large head and neck CT dataset with a total of 699
images from different volunteers, and conduct comprehensive experiments and
comparisons with other state-of-the-art methods to justify the effectiveness
and efficiency of our proposed method. The overall Dice Similarity Coefficient
(DSC) value of 0.842 across the nine important OARs demonstrates great
potential applications in improving the delineation quality and reducing the
time cost.
