Fast, Efficient, and Accurate Neuro-Imaging Denoising via Deep Learning

Abstract

Volumetric functional imaging is widely used for recording neuron activities in vivo, but there exist tradeoffs between the quality of the extracted calcium traces, imaging speed, and laser power. While deep-learning methods have recently been applied to denoise images, their applications to downstream analyses, such as recovering high-SNR calcium traces, have been limited. Further, these methods require temporally-linked pre-registered data with ultrafast rates. Here, we demonstrate supervised deep-denoising methods to circumvent these tradeoffs for several applications, including whole-brain imaging, large field-of-view imaging in freely moving animals, and recovering complex neurite structures in C. elegans. Our framework has 30x smaller memory footprint, and is fast in training and inference (50-70ms); it is highly accurate and generalizable, and further, only small, non-temporally-sequential, independently-acquired training datasets (∼500 images) are needed. We envision that the framework will enable faster and long-term imaging experiments necessary to study neuronal mechanisms of many behaviors.