Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org.

A record of this paper’s transparent peer review process is included in the Supplemental Information.

单细胞分割通常是基于图像的细胞分析的关键任务。我们提出了 nucleAIzer，这是一种深度学习方法，旨在实现一种真正通用的方法，用于在各种分析和光学显微镜模式中定位 2D 细胞核。在代表各种现实条件的图像上，我们优于提交给 2018 年数据科学碗的 739 种方法，其中一些没有在训练数据中表示。我们方法的关键是，在训练期间，nucleAIzer 使用图像风格转移自动将其核风格模型调整为看不见和未标记的数据，以自动生成增强的训练样本。这使模型能够有效地识别新实验和不同实验中的细胞核，而无需专家注释，对于大多数生物光学显微镜实验，使核分割的深度学习相当简单且无需人工。它也可以在线使用，集成到 CellProfiler 中并在 www.nucleaizer.org 免费下载。

本文的透明同行评审过程记录包含在补充信息中。