Akshay Akshay, Katoch Mitali, Abedi Masoud, Besic Mustafa, Shekarchizadeh Navid, Burkhard Fiona C, Bigger-Allen Alex, Adam Rosalyn M, Monastyrskaya Katia, Gheinani Ali Hashemi
Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, Switzerland.
Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland.
bioRxiv. 2023 Jun 28:2023.06.28.533479. doi: 10.1101/2023.06.28.533479.
In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays.
To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results.
SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan.
近年来，三维（3D）球体模型在科学研究中越来越受欢迎，因为它们提供了更接近生理状态的微环境，可模拟体内条件。事实证明，与传统的二维细胞培养方法相比，使用3D球体分析具有优势，因为它能更好地了解细胞行为、药物疗效和毒性。然而，由于缺乏用于球体图像分析的自动化且用户友好的工具，3D球体分析的应用受到阻碍，这对这些分析的可重复性和通量产生了不利影响。
为了解决这些问题，我们开发了一种名为SpheroScan的基于网络的全自动工具，它使用名为带卷积神经网络的掩码区域（R-CNN）的深度学习框架进行图像检测和分割。为了开发一种可应用于一系列实验条件下球体图像的深度学习模型，我们使用通过IncuCyte活细胞分析系统和传统显微镜捕获的球体图像对该模型进行了训练。使用验证和测试数据集对训练后的模型进行性能评估，结果很有前景。
SpheroScan可轻松分析大量图像，并提供交互式可视化功能，以便更深入地理解数据。我们的工具代表了球体图像分析的重大进展，将促进3D球体模型在科学研究中的广泛应用。SpheroScan的源代码和详细教程可在https://github.com/FunctionalUrology/SpheroScan上获取。
