用于生物图像分析再现性的自动细胞分割。

IF 2.6 Q2 BIOCHEMICAL RESEARCH METHODS Synthetic biology (Oxford, England) Pub Date : 2023-01-01 DOI:10.1093/synbio/ysad001
Michael C Robitaille, Jeff M Byers, Joseph A Christodoulides, Marc P Raphael
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引用次数: 1

摘要

活细胞成像在合成生物学研究中非常普遍,但由于缺乏标准化的图像分析,它在实验室间可重复应用的能力受到阻碍。在这里,我们介绍了一种新的细胞分割方法,作为更广泛的独立验证和验证(IV&V)计划的一部分,旨在表征工程盘基ostelium细胞。标准化图像分析被认为是极具挑战性的:参数优化、算法调整、训练和数据预处理步骤所需的人工判断量对再现性构成了严重挑战。为了实现自动化并帮助消除活细胞图像分析中的偏见,我们开发了一种自监督学习(SSL)方法,该方法可以在没有任何最终用户输入的情况下,直接从活细胞显微镜图像的运动中递归地训练自己,从而提供客观的细胞分割。在这里,我们重点介绍了这种SSL方法用于表征原始IV&V程序的工程盘基骨细胞。这种方法具有高度的通用性,可以接受来自任何细胞类型或光学模态的图像,而无需手动训练或参数优化。该方法代表了自动化生物图像分析软件的重要一步,反映了设计可访问的测量技术以提高合成生物学研究的可重复性的更广泛的努力。
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Automated cell segmentation for reproducibility in bioimage analysis.

Live-cell imaging is extremely common in synthetic biology research, but its ability to be applied reproducibly across laboratories can be hindered by a lack of standardized image analysis. Here, we introduce a novel cell segmentation method developed as part of a broader Independent Verification & Validation (IV&V) program aimed at characterizing engineered Dictyostelium cells. Standardizing image analysis was found to be highly challenging: the amount of human judgment required for parameter optimization, algorithm tweaking, training and data pre-processing steps forms serious challenges for reproducibility. To bring automation and help remove bias from live-cell image analysis, we developed a self-supervised learning (SSL) method that recursively trains itself directly from motion in live-cell microscopy images without any end-user input, thus providing objective cell segmentation. Here, we highlight this SSL method applied to characterizing the engineered Dictyostelium cells of the original IV&V program. This approach is highly generalizable, accepting images from any cell type or optical modality without the need for manual training or parameter optimization. This method represents an important step toward automated bioimage analysis software and reflects broader efforts to design accessible measurement technologies to enhance reproducibility in synthetic biology research.

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