Despite that Convolutional Neural Networks (CNNs) have achieved promising performance in many medical image segmentation tasks, they rely on a large set of labeled images for training, which is expensive and time-consuming to acquire. Semi-supervised learning has shown the potential to alleviate this challenge by learning from a large set of unlabeled images and limited labeled samples. In this work, we present a simple yet efficient consistency regularization approach for semi-supervised medical image segmentation, called Uncertainty Rectified Pyramid Consistency (URPC). Inspired by the pyramid feature network, we chose a pyramid-prediction network that obtains a set of segmentation predictions at different scales. For semi-supervised learning, URPC learns from unlabeled data by minimizing the discrepancy between each of the pyramid predictions and their average. We further present multi-scale uncertainty rectification to boost the pyramid consistency regularization, where the rectification seeks to temper the consistency loss at outlier pixels that may have substantially different predictions than the average, potentially due to upsampling errors or lack of enough labeled data. Experiments on two public datasets and an in-house clinical dataset showed that: 1) URPC can achieve large performance improvement by utilizing unlabeled data and 2) Compared with five existing semi-supervised methods, URPC achieved better or comparable results with a simpler pipeline. Furthermore, we build a semi-supervised medical image segmentation codebase to boost research on this topic: https://github.com/HiLab-git/SSL4MIS.

尽管卷积神经网络 (CNN) 在许多医学图像分割任务中取得了可喜的性能，但它们依赖于大量的标记图像进行训练，这既昂贵又耗时。半监督学习已经显示出通过从大量未标记图像和有限标记样本中学习来缓解这一挑战的潜力。在这项工作中，我们提出了一种用于半监督医学图像分割的简单而有效的一致性正则化方法，称为不确定性校正金字塔一致性 (URPC)。受金字塔特征网络的启发，我们选择了一个金字塔预测网络，该网络获得了一组不同尺度的分割预测。对于半监督学习，URPC 通过最小化每个金字塔预测与其平均值之间的差异来从未标记的数据中学习。我们进一步提出了多尺度不确定性校正以提高金字塔一致性正则化，其中校正旨在缓和异常像素的一致性损失，这些像素的预测可能与平均值大不相同，这可能是由于上采样错误或缺乏足够的标记数据。在两个公共数据集和一个内部临床数据集上的实验表明：1）URC 可以通过利用未标记的数据实现较大的性能提升；2）与现有的五种半监督方法相比，URPC 以更简单的管道取得了更好或可比的结果。此外，我们建立了一个半监督的医学图像分割代码库来促进对该主题的研究：https://github。