  Semi-supervised segmentation remains challenging in medical imaging since the
amount of annotated medical data is often scarce and there are many blurred
pixels near the adhesive edges or in the low-contrast regions. To address the
issues, we advocate to firstly constrain the consistency of pixels with and
without strong perturbations to apply a sufficient smoothness constraint and
further encourage the class-level separation to exploit the low-entropy
regularization for the model training. Particularly, in this paper, we propose
the SS-Net for semi-supervised medical image segmentation tasks, via exploring
the pixel-level smoothness and inter-class separation at the same time. The
pixel-level smoothness forces the model to generate invariant results under
adversarial perturbations. Meanwhile, the inter-class separation encourages
individual class features should approach their corresponding high-quality
prototypes, in order to make each class distribution compact and separate
different classes. We evaluated our SS-Net against five recent methods on the
public LA and ACDC datasets. Extensive experimental results under two
semi-supervised settings demonstrate the superiority of our proposed SS-Net
model, achieving new state-of-the-art (SOTA) performance on both datasets. The
code is available at https://github.com/ycwu1997/SS-Net.
