Current deep learning-based approaches for the segmentation of microscopy images heavily rely on large amount of training data with dense annotation, which is highly costly and laborious in practice. Compared to full annotation where the complete contour of objects is depicted, point annotations, specifically object centroids, are much easier to acquire and still provide crucial information about the objects for subsequent segmentation. In this paper, we assume access to point annotations only during training and develop a unified pipeline for microscopy image segmentation using synthetically generated training data. Our framework includes three stages: (1) it takes point annotations and samples a pseudo dense segmentation mask constrained with shape priors; (2) with an image generative model trained in an unpaired manner, it translates the mask to a realistic microscopy image regularized by object level consistency; (3) the pseudo masks along with the synthetic images then constitute a pairwise dataset for training an ad-hoc segmentation model. On the public MoNuSeg dataset, our synthesis pipeline produces more diverse and realistic images than baseline models while maintaining high coherence between input masks and generated images. When using the identical segmentation backbones, the models trained on our synthetic dataset significantly outperform those trained with pseudo-labels or baseline-generated images. Moreover, our framework achieves comparable results to models trained on authentic microscopy images with dense labels, demonstrating its potential as a reliable and highly efficient alternative to labor-intensive manual pixel-wise annotations in microscopy image segmentation. The code is available.

翻译：当前基于深度学习的显微图像分割方法严重依赖大量带有密集标注的训练数据，这在实践中成本高昂且耗时费力。相较于需要描绘物体完整轮廓的全标注，点标注（尤其是物体质心）更易获取，且仍能为后续分割提供关键目标信息。本文假设训练阶段仅能获取点标注，并开发了基于合成训练数据的统一显微图像分割流程。该框架包含三个阶段：(1) 以点标注为输入，通过形状先验约束采样生成伪密集分割掩膜；(2) 利用未配对训练的图像生成模型，将掩膜转化为经目标级一致性正则化的真实感显微图像；(3) 将伪掩膜与合成图像配对，构建用于训练专用分割模型的有标注数据集。在公开MoNuSeg数据集上，本合成流程在保持输入掩膜与生成图像高度一致性的前提下，生成的图像比基线模型更具多样性和真实感。采用相同分割骨干网络时，基于本合成数据集训练的模型性能显著优于使用伪标签或基线生成图像的模型。此外，本框架取得了与使用密集标注真实显微图像训练的模型相当的效果，证明了其作为显微图像分割中劳动密集型逐像素手动标注的可靠高效替代方案的潜力。相关代码已开源。