# Denoising Diffusion Probabilistic Models for Generation of Realistic Fully-Annotated Microscopy Image Data Sets

arXiv: https://arxiv.org/abs/2301.10227 · HTML: https://mlsanity.com/paper/2301.10227/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 16 of its 48 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation](https://mlsanity.com/paper/2211.03364/) (2022)
- [Models Genesis](https://mlsanity.com/paper/2004.07882/) (2020)
- [Implicit Neural Representations for Generative Modeling of Living Cell Shapes](https://mlsanity.com/paper/2207.06283/) (2022)
- [3D fluorescence microscopy data synthesis for segmentation and benchmarking](https://mlsanity.com/paper/2107.10180/) (2021)
- [CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANs](https://mlsanity.com/paper/2010.12011/) (2020)
- [Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learning](https://mlsanity.com/paper/2112.08837/) (2021)
- [Robust 3D Cell Segmentation: Extending the View of Cellpose](https://mlsanity.com/paper/2105.00794/) (2021)

## Built on by (most cited first, 13 of 13)

- [Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning](https://mlsanity.com/paper/2307.04726/) (2023)
- [AnyStar: Domain randomized universal star-convex 3D instance segmentation](https://mlsanity.com/paper/2307.07044/) (2023)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [A Pragmatic Note on Evaluating Generative Models with Fr'echet Inception Distance for Retinal Image Synthesis](https://mlsanity.com/paper/2502.17160/) (2025)
- [Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields](https://mlsanity.com/paper/2403.17808/) (2024)
- [Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation](https://mlsanity.com/paper/2311.05479/) (2023)
- [Improving 3D deep learning segmentation with biophysically motivated cell synthesis](https://mlsanity.com/paper/2408.16471/) (2024)
- [Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation](https://mlsanity.com/paper/2411.11515/) (2024)
- [CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfer](https://mlsanity.com/paper/2503.08603/) (2025)
- [Illumination and Shadows in Head Rotation: experiments with Denoising Diffusion Models](https://mlsanity.com/paper/2308.06057/) (2023)
- [Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI](https://mlsanity.com/paper/2404.07377/) (2024)
- [Synthetic dual image generation for reduction of labeling efforts in semantic segmentation of micrographs with a customized metric function](https://mlsanity.com/paper/2408.00707/) (2024)
- [Dual-Control Frequency-Aware Diffusion Model for Depth-Dependent Optical Microrobot Microscopy Image Generation](https://mlsanity.com/paper/2604.11680/) (2026)

## Abstract (arXiv)

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.