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Semantic segmentation of microscopic cell images using deep learning is animportant technique, however, it requires a large number of images and groundtruth labels for training. To address the above problem, we consider anefficient learning framework with as little data as possible, and we proposetwo types of learning strategies: One-shot segmentation which can learn withonly one training sample, and Partially-supervised segmentation which assignsannotations to only a part of images. Furthermore, we introduce novelsegmentation methods using the small prompt images inspired by prompt learningin recent studies. Our proposed methods use a pre-trained model based on onlycell images and teach the information of the prompt pairs to the target imageto be segmented by the attention mechanism, which allows for efficient learningwhile reducing the burden of annotation costs. Through experiments conducted onthree types of microscopic cell image datasets, we confirmed that the proposedmethod improved the Dice score coefficient (DSC) in comparison with theconventional methods.

One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt