In voltage imaging, where the membrane potentials of individual neurons are recorded at from hundreds to thousand frames per second using fluorescence microscopy, data processing presents a challenge. Even a fraction of a minute of recording with a limited image size yields gigabytes of video data consisting of tens of thousands of frames, which can be time-consuming to process. Moreover, millisecond-level short exposures lead to noisy video frames, obscuring neuron footprints especially in deep-brain samples where noisy signals are buried in background fluorescence. To address this challenge, we propose a fast neuron segmentation method able to detect multiple, potentially overlapping, spiking neurons from noisy video frames, and implement a data processing pipeline incorporating the proposed segmentation method along with GPU-accelerated motion correction. By testing on existing datasets as well as on new datasets we introduce, we show that our pipeline extracts neuron footprints that agree well with human annotation even from cluttered datasets, and demonstrate real-time processing of voltage imaging data on a single desktop computer for the first time.

翻译：在电压成像中，通过荧光显微镜以每秒数百至数千帧的速率记录单个神经元的膜电位，数据处理构成了一项挑战。即使仅记录不到一分钟、图像尺寸有限的片段，也会产生包含数万帧、数据量达千兆字节的视频数据，处理这些数据极为耗时。此外，毫秒级的短曝光时间导致视频帧噪声较大，尤其是在深层脑组织样本中，淹没在背景荧光中的噪声信号使神经元轮廓难以辨识。为应对这一挑战，我们提出了一种快速神经元分割方法，能够从含噪视频帧中检测多个可能重叠的脉冲神经元，并构建了一个整合所提分割方法与GPU加速运动校正的数据处理流程。通过在既有数据集及我们引入的新数据集上进行测试，我们证明该流程能够从即使高度杂乱的图像中提取出与人工标注高度吻合的神经元轮廓，并首次在单台桌面计算机上实现了电压成像数据的实时处理。