High-resolution Ca²⁺ imaging to study cellular Ca²⁺ behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization of Ca²⁺ signals as a function of time and space are often used. Methods of STMap analysis rely on a highly arduous process of user defined segmentation and event-based data retrieval. These methods are often time consuming, lack accuracy, and are extremely variable between users. We designed a novel automated machine-learning based plugin for the analysis of Ca²⁺ STMaps (STMapAuto). The plugin includes optimized tools for Ca²⁺ signal preprocessing, automated segmentation, and automated extraction of key Ca²⁺ event information such as duration, spatial spread, frequency, propagation angle, and intensity in a variety of cell types including the Interstitial cells of Cajal (ICC). The plugin is fully implemented in Fiji and able to accurately detect and expeditiously quantify Ca²⁺ transient parameters from ICC. The plugin’s speed of analysis of large-datasets was 197-fold faster than the commonly used single pixel-line method of analysis. The automated machine-learning based plugin described dramatically reduces opportunities for user error and provides a consistent method to allow high-throughput analysis of STMap datasets.

用于研究细胞 Ca ²⁺行为的高分辨率 Ca ²⁺成像已导致创建大型数据集，非常需要标准化和准确的分析。为了分析这些数据集，经常使用时空图 (STMap)，它允许将 Ca ²⁺信号作为时间和空间的函数进行 2D 可视化。STMap 分析方法依赖于用户定义的分割和基于事件的数据检索的高度艰巨的过程。这些方法通常很耗时，缺乏准确性，并且在用户之间变化很大。我们设计了一种新颖的基于自动化机器学习的插件，用于分析 Ca ²⁺ STMap (STMapAuto)。该插件包括针对 Ca ^{2+ 的}优化工具信号预处理、自动分割和自动提取关键 Ca ²⁺事件信息，如持续时间、空间传播、频率、传播角度和各种细胞类型的强度，包括 Cajal (ICC) 间质细胞。该插件在斐济完全实施，能够准确检测和快速量化来自 ICC 的Ca ²⁺瞬态参数。该插件对大型数据集的分析速度比常用的单像素线分析方法快 197 倍。所描述的基于自动化机器学习的插件极大地减少了用户出错的机会，并提供了一种一致的方法来允许对 STMap 数据集进行高通量分析。