  Gross Target Volume (GTV) segmentation plays an irreplaceable role in
radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that
Convolutional Neural Networks (CNN) have achieved good performance for this
task, they rely on a large set of labeled images for training, which is
expensive and time-consuming to acquire. In this paper, we propose a novel
framework with Uncertainty Rectified Pyramid Consistency (URPC) regularization
for semi-supervised NPC GTV segmentation. Concretely, we extend a backbone
segmentation network to produce pyramid predictions at different scales. The
pyramid predictions network (PPNet) is supervised by the ground truth of
labeled images and a multi-scale consistency loss for unlabeled images,
motivated by the fact that prediction at different scales for the same input
should be similar and consistent. However, due to the different resolution of
these predictions, encouraging them to be consistent at each pixel directly has
low robustness and may lose some fine details. To address this problem, we
further design a novel uncertainty rectifying module to enable the framework to
gradually learn from meaningful and reliable consensual regions at different
scales. Experimental results on a dataset with 258 NPC MR images showed that
with only 10% or 20% images labeled, our method largely improved the
segmentation performance by leveraging the unlabeled images, and it also
outperformed five state-of-the-art semi-supervised segmentation methods.
Moreover, when only 50% images labeled, URPC achieved an average Dice score of
82.74% that was close to fully supervised learning.
