Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF.

翻译：基于卷积神经网络的监督学习算法已成为医学图像分割任务的基准，但其有效性严重依赖于大量标注数据。然而，医学图像数据集的标注过程耗时且费力。受利用标注和未标注数据进行训练的半监督算法启发，我们提出PLGDF框架，该框架基于平均教师网络，通过更少的标注实现医学图像分割。我们提出一种新颖的伪标签利用方案，通过结合标注与未标注数据有效增强数据集。此外，我们在分割网络解码器模块中强制不同尺度间的一致性，并提出适用于评估一致性的损失函数。同时，我们对预测结果进行锐化操作，进一步提升分割精度。在三个公开数据集上的大量实验表明，PLGDF框架通过引入未标注数据可大幅提升性能。此外，与六种最先进的半监督学习方法相比，我们的框架取得了更优性能。本研究的代码可在 https://github.com/ortonwang/PLGDF 获取。