  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.
