  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.
