  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.
