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.
翻译:毛目标量( GTV) 毛目标量( GTV) 分解在Nopharyanngal Carcinoma (NPC) 的放射治疗规划中发挥着不可替代的作用。 尽管进化神经网络( PPNet) 已经取得了良好的业绩, 但它们依赖大量标签图像进行培训, 而培训则需要花费昂贵且耗时才能获得。 在本文中, 我们提出一个新框架, 包含不确定性的校正金字塔( URPC) 的半受监管的 NPC GTV 分解( URPC) 常规化。 具体地说, 我们扩展了一个骨干分解网络, 以在不同比例上生成金字塔图象( CNNNNNNet) 。 金字塔预测网络( PPNet) 受标签图像( CNNNNNN) 的地面真实性能监督, 以及无标签图像的多级一致性损失。 然而, 由于这些预测的不同分辨率分辨率, 我们鼓励每个像素分解的每个像体都保持不那么强, 。 为了精确的细节。 为了解决这个问题, 我们进一步设计一个不精确的模变近的模模模模模模模模模模版模型, 。