Synthesis of Annotated Colorectal Cancer Tissue Images from Gland Layout
This work addresses the need for synthetic annotated images in computational histopathology, particularly for training and evaluating algorithms in colorectal cancer analysis, though it appears incremental as it builds on existing generative models.
The paper tackles the challenge of generating realistic colorectal cancer tissue images with glandular masks by proposing an interactive framework that allows user control over gland parameters, achieving good FID scores compared to state-of-the-art models and demonstrating utility for evaluating gland segmentation algorithms.
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.