Open AccessSegmentation of neurons from fluorescence calcium recordings beyond real time
Author(s) -
Yijun Bao,
Somayyeh Soltanian-Zadeh,
Sina Farsiu,
Yiyang Gong
Publication year - 2021
Publication title -
nature machine intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.894
H-Index - 16
ISSN - 2522-5839
DOI - 10.1038/s42256-021-00342-x
Subject(s) - calcium imaging , segmentation , computer science , artificial intelligence , spike (software development) , two photon excitation microscopy , neuron , ground truth , pattern recognition (psychology) , premovement neuronal activity , microscopy , fluorescence , computer vision , neuroscience , biological system , calcium , physics , chemistry , biology , optics , software engineering , organic chemistry
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 acquired by independent experimental groups; the difference in accuracy was enlarged when processing datasets containing few manually marked ground truths. We also developed an online version, potentially enabling real-time feedback neuroscience experiments.