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生物影像分析开源软件的发展:机遇与挑战。

Developing open-source software for bioimage analysis: opportunities and challenges.

机构信息

Univ. Bordeaux, CNRS, Interdisciplinary Institute for Neuroscience, IINS, UMR 5297, Bordeaux, 33000, France.

Univ. Bordeaux, CNRS, INSERM, Bordeaux Imaging Center, BIC, UMS 3420, US 4, Bordeaux, 33000, France.

出版信息

F1000Res. 2021 Apr 19;10:302. doi: 10.12688/f1000research.52531.1. eCollection 2021.

Abstract

Fast-paced innovations in imaging have resulted in single systems producing exponential amounts of data to be analyzed. Computational methods developed in computer science labs have proven to be crucial for analyzing these data in an unbiased and efficient manner, reaching a prominent role in most microscopy studies. Still, their use usually requires expertise in bioimage analysis, and their accessibility for life scientists has therefore become a bottleneck. Open-source software for bioimage analysis has developed to disseminate these computational methods to a wider audience, and to life scientists in particular. In recent years, the influence of many open-source tools has grown tremendously, helping tens of thousands of life scientists in the process. As creators of successful open-source bioimage analysis software, we here discuss the motivations that can initiate development of a new tool, the common challenges faced, and the characteristics required for achieving success.

摘要

影像学的快速创新导致单个系统产生了大量需要分析的数据。计算机科学实验室开发的计算方法已被证明对于以公正和高效的方式分析这些数据至关重要,在大多数显微镜研究中发挥了重要作用。尽管如此,它们的使用通常需要生物图像分析方面的专业知识,因此,生命科学家对它们的使用变得受到限制。用于生物图像分析的开源软件已被开发出来,以将这些计算方法传播给更广泛的受众,特别是生命科学家。近年来,许多开源工具的影响力大大增强,在这一过程中帮助了成千上万的生命科学家。作为成功的开源生物图像分析软件的创建者,我们在这里讨论可以启动新工具开发的动机、面临的常见挑战以及取得成功所需的特点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9043/8226416/2f27c10b9259/f1000research-10-55826-g0000.jpg

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