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.

在电压成像中，我们提出了一种快速神经元分割方法，能够从噪声视频帧中检测到多个可能重叠的尖峰神经元，并实现了包括所提出的分割方法和 GPU 加速的运动校正在内的数据处理流程。通过在现有数据集和新数据集上的测试，我们展示了我们的处理流程能够从混乱的数据集中提取与人工标注良好一致的神经元轮廓，并首次展示了在单台台式电脑上实现电压成像数据的实时处理。