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.

翻译：使用深度学习进行显微细胞图像的语义分割是一项重要技术，但该方法需要大量图像及对应的真实标签进行训练。为解决上述问题，我们探索了一种在极少量数据条件下实现高效学习的框架，并提出了两种学习策略：仅需单张训练样本即可学习的一次性分割，以及仅对部分图像分配标注的部分监督分割。此外，受近期研究中提示学习的启发，我们引入了基于小提示图像的新型分割方法。所提方法采用仅由细胞图像预训练的模型，通过注意力机制将提示对中的信息传递至待分割的目标图像，从而在降低标注成本的同时实现高效学习。在三种显微细胞图像数据集上的实验证实，与传统方法相比，所提方法显著提升了Dice相似系数（DSC）。