PDF(Pubmed)
Abstract：
Cell culture is undeniably important for multiple scientific applications, including pharmaceuticals, transplants, and cosmetics. However, cell culture involves multiple manual steps, such as regularly analyzing cell images for their health and morphology. Computer scientists have developed algorithms to automate cell imaging analysis, but they are not widely adopted by biologists, especially those lacking an interactive platform. To address the issue, we compile and review existing open-source cell image processing tools that provide interactive interfaces for management and prediction tasks. We highlight the prediction tools that can detect, segment, and track different mammalian cell morphologies across various image modalities and present a comparison of algorithms and unique features of these tools, whether they work locally or in the cloud. This would guide non-experts to determine which is best suited for their purposes and, developers to acknowledge what is worth further expansion. In addition, we provide a general discussion on potential implementations of the tools for a more extensive scope, which guides the reader to not restrict them to prediction tasks only. Finally, we conclude the article by stating new considerations for the development of interactive cell imaging tools and suggesting new directions for future research.
摘要：
细胞培养对于多种科学应用无疑是重要的，包括药品,移植,和化妆品。然而,细胞培养涉及多个手动步骤，例如定期分析细胞图像的健康和形态。计算机科学家已经开发了算法来自动化细胞成像分析，但是它们并没有被生物学家广泛采用，尤其是那些缺乏互动平台的人。为了解决这个问题，我们编译并审查了现有的开源细胞图像处理工具，这些工具为管理和预测任务提供了交互式界面。我们强调了可以检测的预测工具，段,并在各种图像模式中跟踪不同的哺乳动物细胞形态，并对这些工具的算法和独特功能进行比较，无论是在本地还是在云中工作。这将指导非专家确定哪个最适合他们的目的，开发商承认什么是值得进一步扩展。此外,我们对更广泛的工具的潜在实现进行了一般性讨论，这引导读者不要将它们仅限于预测任务。最后,最后，我们通过阐述交互式细胞成像工具发展的新考虑因素，并提出未来研究的新方向。
