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<p>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.</p>

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Segment Anything for Microscopy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月12日

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  • Anwai Archit,
  • Luca Freckmann,
  • Sushmita Nair,
  • Nabeel Khalid,
  • Paul Hilt,
  • Vikas Rajashekar,
  • Marei Freitag,
  • Carolin Teuber,
  • Melanie Spitzner,
  • Constanza Tapia Contreras,
  • Genevieve Buckley,
  • Sebastian von Haaren,
  • Sagnik Gupta,
  • Marian Grade,
  • Matthias Wirth,
  • Günter Schneider,
  • Andreas Dengel,
  • Sheraz Ahmed,
  • Constantin Pape

摘要

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.