  Deep Learning (DL) models have been successfully applied to many applications
including biomedical cell segmentation and classification in histological
images. These models require large amounts of annotated data which might not
always be available, especially in the medical field where annotations are
scarce and expensive. To overcome this limitation, we propose a novel pipeline
for generating synthetic datasets for cell segmentation. Given only a handful
of annotated images, our method generates a large dataset of images which can
be used to effectively train DL instance segmentation models. Our solution is
designed to generate cells of realistic shapes and placement by allowing
experts to incorporate domain knowledge during the generation of the dataset.
