Segmentation of Neurons from Fluorescence Calcium Recordings Beyond Real-time.

ABSTRACT: Fluorescent genetically encoded calcium indicators and two-photon microscopy help understand brain function by generating large-scale in vivo recordings in multiple animal models. Automatic, fast, and accurate active neuron segmentation is critical when processing these videos. In this work, we developed and characterized a novel method, Shallow U-Net Neuron Segmentation (SUNS), to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques when processing multiple datasets

SUBMITTER: Bao Y 

PROVIDER: S-EPMC8415119 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature