CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.
CellSNAP: A fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.
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CellSNAP：一种快速、准确的算法，用于定量相位成像中的 3D 细胞分割。
DOI：
10.1101/2023.07.24.550376
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Barman,Ishan
中科院分区：
文献类型：
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作者：
Raj,Piyush;Paidi,Santosh;Conway,Lauren;Chatterjee,Arnab;Barman,Ishan
意义三维定量相位成像（QPI）已迅速成为荧光成像的补充工具，因为它提供了细胞形态和动力学的客观测量，并且不受造影剂的影响。它通过提供各种细胞参数的系统和相关分析而不受光漂白和光毒性的限制，开辟了新的研究方向。虽然当前的 QPI 系统可以快速采集断层图像，但分析这些原始三维 (3D) 断层图像的流程尚未完善。我们重点关注分析流程中一个关键但经常被低估的步骤，即从采集的断层图像中进行 3D 细胞分割。目的我们报告了用于 QPI 图像 3D 分割的 CellSNAP（通过相位成像新颖算法进行细胞分割）算法。方法细胞分割算法模仿宝石提取过程，从二维 (2D) 分割掩模进行粗略 3D 挤压以勾勒出细胞结构。生成 2D 图像，并且分割算法识别平面中的边界。利用连续堆叠中的细胞连续性，精细的 3D 分割（类似于宝石雕刻中的精细凿刻）完成了该过程。结果 CellSNAP 算法在速度、鲁棒性和实施方面超越了当前的黄金标准，在单核处理器上实现了每个细胞 2 秒以下的细胞分割。 CellSNAP 的实施可以轻松地在多核系统上并行化，以进一步提高速度。对于可以使用现有标准方法进行分割的情况，我们的算法显示干质量的平均差异为 5%，体积测量的平均差异为 8%。我们还表明，CellSNAP 可以处理具有挑战性的图像数据集，其中细胞因干涉图漂移而聚集和损坏，这给所有以 QPI 为重点的基于 AI 的分割工具带来了主要困难。结论我们提出的方法内存密集程度较低，并且比现有方法更快。该方法可以在学生笔记本电脑上轻松实现。由于该方法是基于规则的，因此不需要收集大量成像数据并手动注释它们来执行基于机器学习的模型训练。我们预计我们的工作将导致更广泛地采用 QPI 成像进行高通量分析，这在一定程度上因缺乏合适的图像分割工具而受到阻碍。
SignificanceThree-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms.AimWe report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images.ApproachThe cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in theplane. Leveraging cell continuity in consecutive-stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process.ResultsThe CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2 s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools.ConclusionOur proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.