Two-photon calcium imaging provides large-scale recordings of neuronal activities at cellular resolution. A robust, automated and high-speed pipeline to simultaneously segment the spatial footprints of neurons and extract their temporal activity traces while decontaminating them from background, noise and overlapping neurons is highly desirable to analyse calcium imaging data. Here we demonstrate DeepCaImX, an end-to-end deep learning method based on an iterative shrinkage-thresholding algorithm and a long short-term memory neural network to achieve the above goals altogether at a very high speed and without any manually tuned hyperparameter. DeepCaImX is a multi-task, multi-class and multi-label segmentation method composed of a compressed sensing-inspired neural network with a recurrent layer and fully connected layers. The neural network can simultaneously generate accurate neuronal footprints and extract clean neuronal activity traces from calcium imaging data. We trained the neural network with simulated datasets and benchmarked it against existing state-of-the-art methods with in vivo experimental data. DeepCaImX outperforms existing methods in the quality of segmentation and temporal trace extraction as well as processing speed. DeepCaImX is highly scalable and will benefit the analysis of mesoscale calcium imaging.

双光子钙成像以细胞分辨率提供神经元活动的大规模记录。分析钙成像数据非常需要一个强大的、自动化的高速管道来同时分割神经元的空间足迹并提取其时间活动轨迹，同时清除背景、噪声和重叠神经元的污染。在这里，我们演示了 DeepCaImX，一种基于迭代收缩阈值算法和长短期记忆神经网络的端到端深度学习方法，可以以非常高的速度完全实现上述目标，并且无需任何手动调整的超参数。 DeepCaImX 是一种多任务、多类和多标签分割方法，由具有循环层和全连接层的压缩感知启发神经网络组成。神经网络可以同时生成准确的神经元足迹，并从钙成像数据中提取干净的神经元活动痕迹。我们使用模拟数据集训练神经网络，并使用体内实验数据将其与现有最先进的方法进行基准测试。 DeepCaImX 在分割和时间轨迹提取的质量以及处理速度方面优于现有方法。 DeepCaImX 具有高度可扩展性，将有利于介观钙成像的分析。