美国麻省理工学院和哈佛大学博德研究所Anne E. Carpenter研究组报道了2018年数据科学碗锦标赛的结果：不同成像实验中的细胞核分割。10月21日，《自然—方法学》在线发表了这项成果。

2018年数据科学碗锦标赛吸引了全球3891个团队，首次尝试建立一种分割方法，其可以应用于整个实验中染色核的任何二维光学显微镜图像，而无需人工干预。挑战中的顶级参与者成功完成了这项任务，他们开发了基于深度学习的模型，该模型可以识别许多图像类型和实验条件下的细胞核，而无需手动调整分割参数。这是迈向无配置生物图像分析软件工具的重要一步。

据了解，在显微镜图像中分割细胞核通常是对生物学和生物医学应用的成像数据进行定量分析的第一步。许多生物图像分析工具可以分割图像中的核，但需要为每个实验进行选择和配置。

附：英文原文

Title: Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl

Author: Juan C. Caicedo, Allen Goodman, Kyle W. Karhohs, Beth A. Cimini, Jeanelle Ackerman, Marzieh Haghighi, CherKeng Heng, Tim Becker, Minh Doan, Claire McQuin, Mohammad Rohban, Shantanu Singh & Anne E. Carpenter

Issue&Volume: 2019-10-21

Abstract: 

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.

DOI: 10.1038/s41592-019-0612-7

Source: https://www.nature.com/articles/s41592-019-0612-7