ArticlePLoS computational biology2024
Denoising diffusion probabilistic models for generation of realistic fully-annotated microscopy image datasets.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.Cell reports methods · 2026Article
- Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026Article
- Universal consensus 3D segmentation of cells from 2D segmented stacks.Nature methods · 2025Article
- SpaDiff: Denoising for Sequence-based Spatial Transcriptomics via Diffusion Process.bioRxiv : the preprint server for biology · 2025Article
- HindwingLib: A library of leaf beetle hindwings generated by Stable Diffusion and ControlNet.Scientific data · 2025Article
- A data-efficient strategy for building high-performing medical foundation models.Nature biomedical engineering · 2025Article
- Deep generative modeling of annotated bacterial biofilm images.NPJ biofilms and microbiomes · 2025Article
- Improving 3D deep learning segmentation with biophysically motivated cell synthesis.Communications biology · 2025Article
- Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis.NPJ digital medicine · 2024Article
- AnyStar: Domain randomized universal star-convex 3D instance segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
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Authors and funding
8 authors.
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Abstract
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. We demonstrate that segmentation models trained with a small set of synthetic image data reach accuracy levels comparable to those of generalist models trained with a large and diverse collection of manually annotated image data, thereby offering a streamlined and specialized application of segmentation models.
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