Automatic detection and segmentation of biological objects in 2D and 3D image data is central for countless biomedical research questions to be answered. While many existing computational methods are used to reduce manual labeling time, there is still a huge demand for further quality improvements of automated solutions. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility to biomedical data is largely unexplored. Here we introduce EmbedSeg, an embedding-based instance segmentation method designed to segment instances of desired objects visible in 2D or 3D biomedical image data. We apply our method to four 2D and seven 3D benchmark datasets, showing that we either match or outperform existing state-of-the-art methods. While the 2D datasets and three of the 3D datasets are well known, we have created the required training data for four new 3D datasets, which we make publicly available online. Next to performance, also usability is important for a method to be useful. Hence, EmbedSeg is fully open source (https://github.com/juglab/EmbedSeg), offering $\left(i\right)$ tutorial notebooks to train EmbedSeg models and use them to segment object instances in new data, and $\left(ii\right)$ a napari plugin that can also be used for training and segmentation without requiring any programming experience. We believe that this renders EmbedSeg accessible to virtually everyone who requires high-quality instance segmentations in 2D or 3D biomedical image data.

自动检测和分割 2D 和 3D 图像数据中的生物对象是解决无数生物医学研究问题的核心。虽然许多现有的计算方法用于减少手动标记时间，但仍然存在对进一步提高自动化解决方案质量的巨大需求。在自然图像领域，众所周知，基于空间嵌入的实例分割方法可以产生高质量的结果，但它们在生物医学数据中的实用性在很大程度上尚未得到探索。这里我们介绍EmbedSeg，一种基于嵌入的实例分割方法，旨在分割 2D 或 3D 生物医学图像数据中可见的所需对象的实例。我们将我们的方法应用于四个 2D 和七个 3D 基准数据集，表明我们匹配或优于现有的最先进方法。虽然 2D 数据集和三个 3D 数据集是众所周知的，但我们已经为四个新的 3D 数据集创建了所需的训练数据，并在网上公开提供。除了性能之外，可用性对于一种有用的方法也很重要。因此，EmbedSeg是完全开源的 (https://github.com/juglab/EmbedSeg)，提供$\left(\mathrm{一世}\right)$用于训练EmbedSeg模型并使用它们在新数据中分割对象实例的 教程笔记本，以及$\left(\mathrm{一世}\mathrm{一世}\right)$ 一个 napari 插件，无需任何编程经验也可用于训练和分割。我们相信，这使得几乎所有需要在 2D 或 3D 生物医学图像数据中进行高质量实例分割的人都可以使用EmbedSeg 。