  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.
