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PartSeg:傻瓜式 3D 显微镜图像定量特征提取工具。

PartSeg: a tool for quantitative feature extraction from 3D microscopy images for dummies.

机构信息

Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw, Poland.

Institute of Informatics, University of Warsaw, Warsaw, Poland.

出版信息

BMC Bioinformatics. 2021 Feb 17;22(1):72. doi: 10.1186/s12859-021-03984-1.

Abstract

BACKGROUND

Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation.

RESULTS

In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures.

CONCLUSIONS

In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

摘要

背景

生物成像技术为研究各种条件下细胞群体的分子途径和形态表型提供了一种强大的工具。随着现代高分辨率 3D 显微镜提供了越来越多的高质量图像,因此需要以自动化、无偏和简单的方式对其进行分析。细胞核内结构的分割是本文的重点,它呈现出一种新的复杂性形式,即密集包装和显著的信号重叠。同时,现有的分割工具对于技术背景有限的新用户来说,学习曲线陡峭。在图像集的批量处理中尤其明显,这需要使用某种形式的编程表示法。

结果

在本文中,我们提出了 PartSeg,这是一种用于 3D 显微镜图像分割和重建的工具,专门针对细胞核研究进行了优化。PartSeg 集成了几个最先进算法的改进版本,包括一种新的多尺度方法,用于分割和定量分析 3D 显微镜图像。PartSeg 的功能和用户友好界面是根据对多个用例的分析以及对其他工具遇到的困难进行精心规划的,旨在为用户提供一个具有最小进入门槛的符合人体工程学的界面。可以以特定的方式进行批量处理,而无需程序员的支持。随着感兴趣的数据集的大小增长,这种批量处理解决方案对于正确分析结果的统计数据变得至关重要。高级用户可以在 Python 数据处理和可视化管道中使用 PartSeg 组件作为库,例如在 Jupyter 笔记本中。该工具具有可扩展性,因此可以使用插件添加新的功能和算法。对于生物学家,PartSeg 的实用性在几个场景中得到了展示,显示了细胞核结构的定量分析。

结论

在本文中,我们提出了 PartSeg,这是一种用于精确和可验证的 3D 显微镜图像分割和重建的工具。PartSeg 针对细胞核分析进行了优化,并提供了最适合此任务的多尺度分割算法。PartSeg 还可用于多个图像的批量处理,并且其组件可以在其他系统或计算实验中重复使用。

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