Kamran Sharif Amit, Hossain Khondker Fariha, Moghnieh Hussein, Riar Sarah, Bartlett Allison, Tavakkoli Alireza, Sanders Kenton M, Baker Salah A
Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Anderson Medical Building MS352, Reno, NV 89557, USA.
Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA.
iScience. 2022 Apr 21;25(5):104277. doi: 10.1016/j.isci.2022.104277. eCollection 2022 May 20.
Cellular imaging instrumentation advancements as well as readily available optogenetic and fluorescence sensors have yielded a profound need for fast, accurate, and standardized analysis. Deep-learning architectures have revolutionized the field of biomedical image analysis and have achieved state-of-the-art accuracy. Despite these advancements, deep learning architectures for the segmentation of subcellular fluorescence signals is lacking. Cellular dynamic fluorescence signals can be plotted and visualized using spatiotemporal maps (STMaps), and currently their segmentation and quantification are hindered by slow workflow speed and lack of accuracy, especially for large datasets. In this study, we provide a software tool that utilizes a deep-learning methodology to fundamentally overcome signal segmentation challenges. The software framework demonstrates highly optimized and accurate calcium signal segmentation and provides a fast analysis pipeline that can accommodate different patterns of signals across multiple cell types. The software allows seamless data accessibility, quantification, and graphical visualization and enables large dataset analysis throughput.
细胞成像仪器的进步以及现成的光遗传学和荧光传感器，使得对快速、准确和标准化分析产生了迫切需求。深度学习架构彻底改变了生物医学图像分析领域，并达到了当前最先进的精度。尽管有这些进展，但用于亚细胞荧光信号分割的深度学习架构仍然缺乏。细胞动态荧光信号可以使用时空图（STMaps）进行绘制和可视化，目前它们的分割和量化受到工作流程速度慢和缺乏准确性的阻碍，特别是对于大型数据集。在本研究中，我们提供了一种利用深度学习方法从根本上克服信号分割挑战的软件工具。该软件框架展示了高度优化和准确的钙信号分割，并提供了一个快速分析管道，可适应多种细胞类型中不同的信号模式。该软件允许无缝的数据访问、量化和图形可视化，并实现大型数据集的分析通量。
