Few-shot semantic segmentation (FSS) has great potential for medical imaging
applications. Most of the existing FSS techniques require abundant annotated
semantic classes for training. However, these methods may not be applicable for
medical images due to the lack of annotations. To address this problem we make
several contributions: (1) A novel self-supervised FSS framework for medical
images in order to eliminate the requirement for annotations during training.
Additionally, superpixel-based pseudo-labels are generated to provide
supervision; (2) An adaptive local prototype pooling module plugged into
prototypical networks, to solve the common challenging foreground-background
imbalance problem in medical image segmentation; (3) We demonstrate the general
applicability of the proposed approach for medical images using three different
tasks: abdominal organ segmentation for CT and MRI, as well as cardiac
segmentation for MRI. Our results show that, for medical image segmentation,
the proposed method outperforms conventional FSS methods which require manual
annotations for training.

本文提出了一种针对医学图像的新型自监督少样本分割方案，其中使用基于超像素的伪标签提供监督，采用自适应本地原型池化模块解决前景背景不平衡问题，并在三个不同的任务下测试，结果表明该方法优于传统需要手动注释的方法。