  Generating realistic tissue images with annotations is a challenging task
that is important in many computational histopathology applications.
Synthetically generated images and annotations are valuable for training and
evaluating algorithms in this domain. To address this, we propose an
interactive framework generating pairs of realistic colorectal cancer histology
images with corresponding glandular masks from glandular structure layouts. The
framework accurately captures vital features like stroma, goblet cells, and
glandular lumen. Users can control gland appearance by adjusting parameters
such as the number of glands, their locations, and sizes. The generated images
exhibit good Frechet Inception Distance (FID) scores compared to the
state-of-the-art image-to-image translation model. Additionally, we demonstrate
the utility of our synthetic annotations for evaluating gland segmentation
algorithms. Furthermore, we present a methodology for constructing glandular
masks using advanced deep generative models, such as latent diffusion models.
These masks enable tissue image generation through a residual encoder-decoder
network.
