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| Item Type: | Article |
|---|---|
| Title: | Segment anything for microscopy |
| Creators Name: | Archit, A., Freckmann, L., Nair, S., Khalid, N., Hilt, P., Rajashekar, V., Freitag, M., Teuber, C., Spitzner, M., Tapia Contreras, C., Buckley, G., von Haaren, S., Gupta, S., Grade, M., Wirth, M., Schneider, G., Dengel, A., Ahmed, S. and Pape, C. |
| Abstract: | Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models. |
| Keywords: | Algorithms, Computer-Assisted Image Processing, Deep Learning, Electron Microscopy, Microscopy, Software |
| Source: | Nature Methods |
| ISSN: | 1548-7091 |
| Publisher: | Nature Publishing Group |
| Volume: | 22 |
| Number: | 3 |
| Page Range: | 579-591 |
| Date: | March 2025 |
| Additional Information: | Erratum in: Nat Methods 10 June 2025. |
| Official Publication: | https://doi.org/10.1038/s41592-024-02580-4 |
| PubMed: | View item in PubMed |
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