mlsanityData to 7 Oct 2026

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

arXiv 2301.10227 ↗First posted 2 Jan 2023eess.IV cs.CV cs.LGcited by 13

On the map: Diffusion models → Medical imaging and segmentation

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.

Key facts. Cited by 13 papers in the map (data through 7 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.

Builds on

16 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation7 Nov

Other references · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  5. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  6. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  8. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  9. 2019Data augmentation using learned transformations for one-shot medical image segmentationcited by 138
  10. 2020Models Genesiscited by 26
  11. 2022Implicit Neural Representations for Generative Modeling of Living Cell Shapescited by 12
  12. 20213D fluorescence microscopy data synthesis for segmentation and benchmarkingcited by 8
3 more
  1. 2020CellCycleGAN: Spatiotemporal Microscopy Image Synthesis of Cell Populations using Statistical Shape Models and Conditional GANscited by 7
  2. 2021Improving Unsupervised Stain-To-Stain Translation using Self-Supervision and Meta-Learningcited by 7
  3. 2021Robust 3D Cell Segmentation: Extending the View of Cellposecited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 2; 2026: 1.

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  1. 2023Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learningcited by 40
  2. 2023AnyStar: Domain randomized universal star-convex 3D instance segmentationcited by 14
  3. 2024Replication in Visual Diffusion Models: A Survey and Outlookcited by 11
  4. 2025A Pragmatic Note on Evaluating Generative Models with Fr'echet Inception Distance for Retinal Image Synthesiscited by 8
  5. 2024Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fieldscited by 5
  6. 2023Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentationcited by 4
  7. 2024Improving 3D deep learning segmentation with biophysically motivated cell synthesiscited by 4
  8. 2024Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentationcited by 2
  9. 2025CellStyle: Improved Zero-Shot Cell Segmentation via Style Transfercited by 1
  10. 2023Illumination and Shadows in Head Rotation: experiments with Denoising Diffusion Modelscited by 0
  11. 2024Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AIcited by 0
  12. 2024Synthetic dual image generation for reduction of labeling efforts in semantic segmentation of micrographs with a customized metric functioncited by 0
1 more
  1. 2026Dual-Control Frequency-Aware Diffusion Model for Depth-Dependent Optical Microrobot Microscopy Image Generationcited by 0

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Close in wording, no citation either way

  1. 2024Diffusion Models to Enhance the Resolution of Microscopy Images: A Tutorial
  2. 2024Denoising diffusion models for high-resolution microscopy image restoration
  3. 2024Analysing Diffusion Segmentation for Medical Images
  4. 2023Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain
  5. 2023Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
  6. 2023Class-Guided Image-to-Image Diffusion: Cell Painting from Brightfield Images with Class Labels
  7. 2025Revealing Subtle Phenotypes in Small Microscopy Datasets Using Latent Diffusion Models
  8. 2023Factorized Diffusion Architectures for Unsupervised Image Generation and Segmentation