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Semi-supervised Segmentation via Uncertainty Rectified Pyramid Consistency and Its Application to Gross Target Volume of Nasopharyngeal Carcinoma

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. Recently, semi-supervised methods that learn from a small set of labeled images with a large set of unlabeled images have shown potential for dealing with this problem, but it is still challenging to train a high-performance model with the limited number of labeled data. 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) was 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 is not robust and may bring much noise and lead to a performance drop. To deal with this dilemma, we further design a novel uncertainty rectifying module to enable the framework to gradually learn from meaningful and reliable consensual regions at different scales. Extensive experiments on our collected NPC dataset with 258 volumes show that our method can largely improve performance by incorporating the unlabeled data, and this framework achieves a promising result compared with existing semi-supervised methods, which achieves 81.22% of mean DSC and 1.88 voxels of mean ASD on the test set, where the only 20% of the training set were annotated.