Segmentation of neurons from fluorescence calcium recordings beyond real time

• Published in: Nature machine intelligence Vol. 3; no. 7; pp. 590 - 600
• Main Authors: Bao, Yijun, Soltanian-Zadeh, Somayyeh, Farsiu, Sina, Gong, Yiyang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2021; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/1314; 631/114/1564; 631/114/794; 631/378; Accuracy; Algorithms; Calcium; Datasets; Engineering; Experiments; Feature extraction; Fluorescence; Genetic code; Image segmentation; Neural networks; Neurons; Neurosciences; Photons; Real time; Signal processing; Video
• ISSN: 2522-5839; 2522-5839
• DOI: 10.1038/s42256-021-00342-x

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. Here 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. Calcium imaging is a valuable tool for recording in vivo neural activity, but the task of extracting signals of individual neurons is computationally challenging. Bao and colleagues present a U-Net-based method that is both accurate and fast enough to potentially allow real-time processing and closed-loop experiments.