Segment Anything for Microscopy

doi:10.1038/s41592-024-02580-4

2025-02-12

DOI：10.1038/s41592-024-02580-4

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摘要

En 中文

显微镜图像中物体的精确分割仍然是许多研究人员的瓶颈，尽管为此目的开发了众多工具。在此，我们介绍适用于显微镜的分割任意内容（mu SAM），这是一款用于多维显微镜数据分割和追踪的工具。它基于分割任意内容（Segment Anything），一种用于图像分割的视觉基础模型。我们通过微调适用于光学显微镜和电子显微镜的通用模型来扩展其功能，这些模型显著提升了广泛成像条件下的分割质量。我们还在一个napari插件中实现了交互式和自动分割功能，可以加速多样的分割任务，并为不同显微镜模态提供统一的注释解决方案。我们的工作构成了视觉基础模型在显微镜领域的应用，为使用少量强大的深度学习模型解决该领域的图像分析任务奠定了基础。

期刊

Nature Methods

IF:

32.1

论文数:

7.3K

被引数:

12.7W

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