Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets

Dennis Eschweiler, Rüveyda Yilmaz, Matisse Baumann, Ina Laube, Rijo Roy, Abin Jose, Daniel Brückner, Johannes Stegmaier

Abstract: Author summary Modern generative techniques have unlocked the potential to create realistic image data of high quality, prompting the possibility of substituting real image data in segmentation training workflows. Our study highlights the capacity of denoising diffusion probabilistic models to generate high-quality microscopy image data. With adjustments to the generation process, these models can produce realistic fully-annotated image datasets through an intuitive and unsupervised approach. The parameters of the generative pipeline undergo optimization through various evaluations, resulting in synthetic image data that exhibits high PSNR scores. Our practical experiments encompass multiple scenarios, including manual annotations, initial segmentations, and simulations as starting points, demonstrating the versatility of our approach. Importantly, we compare the performance of segmentation models trained on a limited set of synthetic image data with those trained on a vast and diverse collection of manually annotated data, demonstrating the potential of our pipeline to alleviate the reliance on extensive manually annotated datasets. Our approach lays the groundwork for similar applications, thereby promoting the much-needed availability of publicly accessible fully-annotated image datasets and advancing the goal of annotation-free segmentation.