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STracking:一个免费的开源 Python 库,用于粒子跟踪和分析。

STracking: a free and open-source Python library for particle tracking and analysis.

机构信息

SERPICO Project Team, Inria Centre Rennes-Bretagne Atlantique, F-35042 Rennes, France.

SERPICO Project Team, UMR144 CNRS Institut Curie, PSL Research University, F-75005 Paris, France.

出版信息

Bioinformatics. 2022 Jul 11;38(14):3671-3673. doi: 10.1093/bioinformatics/btac365.

Abstract

SUMMARY

Analysis of intra- and extracellular dynamic like vesicles transport involves particle tracking algorithms. The design of a particle tracking pipeline is a routine but tedious task. Therefore, particle dynamics analysis is often performed by combining several pieces of software (filtering, detection, tracking, etc.) requiring many manual operations, and thus leading to poorly reproducible results. Given the new segmentation tools based on deep learning, modularity and interoperability between software have become essential in particle tracking algorithms. A good synergy between a particle detector and a tracker is of paramount importance. In addition, a user-friendly interface to control the quality of estimated trajectories is necessary. To address these issues, we developed STracking, a Python library that allows combining algorithms into standardized particle tracking pipelines.

AVAILABILITY AND IMPLEMENTATION

STracking is available as a Python library using 'pip install' and the source code is publicly available on GitHub (https://github.com/sylvainprigent/stracking). A graphical interface is available using two napari plugins: napari-stracking and napari-tracks-reader. These napari plugins can be installed via the napari plugins menu or using 'pip install'. The napari plugin source codes are available on GitHub (https://github.com/sylvainprigent/napari-tracks-reader, https://github.com/sylvainprigent/napari-stracking).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

摘要

分析细胞内外动态囊泡运输涉及粒子追踪算法。粒子追踪管道的设计是一项常规但繁琐的任务。因此,粒子动力学分析通常是通过结合几个软件(过滤、检测、跟踪等)来完成的,这需要许多人工操作,因此导致结果的可重复性较差。鉴于基于深度学习的新分割工具,软件之间的模块化和互操作性已成为粒子追踪算法的必要条件。粒子探测器和跟踪器之间的良好协同作用至关重要。此外,需要一个用户友好的界面来控制估计轨迹的质量。为了解决这些问题,我们开发了 STracking,这是一个允许将算法组合到标准化粒子追踪管道中的 Python 库。

可用性和实现

STracking 作为一个 Python 库使用 'pip install' 进行安装,其源代码在 GitHub 上公开(https://github.com/sylvainprigent/stracking)。使用两个 napari 插件提供图形界面:napari-stracking 和 napari-tracks-reader。这些 napari 插件可以通过 napari 插件菜单或使用 'pip install' 进行安装。napari 插件的源代码可在 GitHub 上获得(https://github.com/sylvainprigent/napari-tracks-reader、https://github.com/sylvainprigent/napari-stracking)。

补充信息

补充数据可在 Bioinformatics 在线获得。

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