  Recent advances in computer vision have led to significant progress in the
generation of realistic image data, with denoising diffusion probabilistic
models proving to be a particularly effective method. In this study, we
demonstrate that diffusion models can effectively generate fully-annotated
microscopy image data sets through an unsupervised and intuitive approach,
using rough sketches of desired structures as the starting point. The proposed
pipeline helps to reduce the reliance on manual annotations when training deep
learning-based segmentation approaches and enables the segmentation of diverse
datasets without the need for human annotations. This approach holds great
promise in streamlining the data generation process and enabling a more
efficient and scalable training of segmentation models, as we show in the
example of different practical experiments involving various organisms and cell
types.
