Real-time Neuron Segmentation for Voltage Imaging
This enables real-time analysis of voltage imaging data for neuroscientists, though it is incremental as it builds on existing segmentation and motion correction techniques.
The authors tackled the challenge of processing noisy, high-speed voltage imaging data by proposing a fast neuron segmentation method and a GPU-accelerated pipeline, achieving real-time processing on a single desktop computer for the first time.
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.