  Semantic segmentation of microscopic cell images using deep learning is an
important technique, however, it requires a large number of images and ground
truth labels for training. To address the above problem, we consider an
efficient learning framework with as little data as possible, and we propose
two types of learning strategies: One-shot segmentation which can learn with
only one training sample, and Partially-supervised segmentation which assigns
annotations to only a part of images. Furthermore, we introduce novel
segmentation methods using the small prompt images inspired by prompt learning
in recent studies. Our proposed methods use a pre-trained model based on only
cell images and teach the information of the prompt pairs to the target image
to be segmented by the attention mechanism, which allows for efficient learning
while reducing the burden of annotation costs. Through experiments conducted on
three types of microscopic cell image datasets, we confirmed that the proposed
method improved the Dice score coefficient (DSC) in comparison with the
conventional methods.
