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The accurate segmentation of organs-at-risk (OARs) in head and neck CT imagesis 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 aresusceptible to high intra- and inter-variability. To this end, we propose anovel dual shape guided network (DSGnet) to automatically delineate nineimportant OARs in head and neck CT images. To deal with the large shapevariation and unclear boundary of OARs in CT images, we represent the organshape using an organ-specific unilateral inverse-distance map (UIDM) and guidethe segmentation task from two different perspectives: direct shape guidance byfollowing the segmentation prediction and across shape guidance by sharing thesegmentation feature. In the direct shape guidance, the segmentation predictionis not only supervised by the true label mask, but also by the true UIDM, whichis implemented through a simple yet effective encoder-decoder mapping from thelabel space to the distance space. In the across shape guidance, UIDM is usedto facilitate the segmentation by optimizing the shared feature maps. For theexperiments, we build a large head and neck CT dataset with a total of 699images from different volunteers, and conduct comprehensive experiments andcomparisons with other state-of-the-art methods to justify the effectivenessand efficiency of our proposed method. The overall Dice Similarity Coefficient(DSC) value of 0.842 across the nine important OARs demonstrates greatpotential applications in improving the delineation quality and reducing thetime cost.

Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images