Yijun Bao, Somayyeh Soltanian-Zadeh, Sina Farsiu & Yiyang Gong
Nature Machine Intelligence volume 3, pages 590–600 (2021)
[Publisher version][shared readcube link][Supplementary Information]
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. 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