Microscopy Research and Technique· 2026Q2
DINOSim : Training‐Free Semantic Segmentation on Microscopy Images
- 4citations
- Q2SCImago
- 2026year
Short summary
DINOSim, a new training-free method, achieves semantic segmentation on microscopy images by comparing image patch features from a pretrained DINOv2 encoder to a user-selected reference, generating pseudo-labels for segmentation.
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Key points
- DINOSim enables training-free semantic segmentation of microscopy images.
- It uses a pretrained DINOv2 encoder to compare image patch features against a user-selected reference.
- Pseudo-labels generated from feature comparisons guide a k-nearest neighbors framework for segmentation.
- The method achieves performance comparable to supervised approaches without manual labeling.
AI-generated from the title and abstract; the full text is not read.
Abstract
We present DINOSim, a training-free method for segmenting objects in microscopy images without the need for large annotated datasets or additional model training. DINOSim builds on the pretrained DINOv2 image encoder, which captures semantic information from images. By comparing the encoder's features of image patches to those of a user-selected reference, DINOSim generates pseudo-labels that guide semantic segmentation. Subsequently, a k-nearest neighbors framework is used to refine predictions across new images. Our experiments show that DINOSim can effectively segment previously unseen objects in diverse microscopy datasets, offering performance comparable to supervised approaches while avoiding the need for costly manual labeling. We also investigate how different choices of user prompt selection and model size affect accuracy and generalization. To make the method widely accessible, we provide an open-source napari plugin (github.com/AAitorG/napari-DINOSim), enabling researchers to apply DINOSim to their own data easily. Overall, DINOSim offers a fast, flexible and practical solution for bioimage analysis, particularly valuable in resource-constrained settings.
The authors' abstract, as published at the source. Microscopy Research and Technique, 2026 · DOI ↗
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BiophysicsBiochemistry, Genetics and Molecular Biology