Publikation
Segment Anything for Microscopy
Nabeel Khalid; Anwai Archit; Luca Freckmann; Sushmita Nair; Paul Hilt; Vikas Rajashekar; Marei Freitag; Carolin Teuber; Genevieve Buckley; Sebastian von Haaren; Sagnik Gupta; Andreas Dengel; Sheraz Ahmed; Constantin Pape
In: Nature, Vol. 22, Pages 579-591, Nature Methods, 2/2025.
Zusammenfassung
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, 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.