Physics-Based Deep Learning for Imaging Neuronal Activity via Two-Photon and Light Field Microscopy

Light Field Microscopy (LFM) is an imaging technique that offers the opportunity to study fast dynamics in biological systems due to its 3D imaging speed and is particularly attractive for functional neuroimaging. Traditional model-based approaches employed in microscopy for reconstructing 3D images from light-field data are affected by reconstruction artifacts and are computationally demanding. This work introduces a deep neural network for LFM to image neuronal activity under adverse conditions: limited training data, background noise, and scattering mammalian brain tissue. The architecture of the network is obtained by unfolding the ISTA algorithm and is based on the observation that neurons in the tissue are sparse. Our approach is also based on a novel modelling of the imaging system that uses a linear convolutional neural network to fit the physics of the acquisition process. We train the network in a semi-supervised manner based on an adversarial training framework. The small labelled dataset required for training is acquired from a single sample via two-photon microscopy, a point-scanning 3D imaging technique that achieves high spatial resolution and deep tissue penetration but at a lower speed than LFM. We introduce physics knowledge of the system in the design of the network architecture and during training to complete our semi-supervised approach. We experimentally show that in the proposed scenario, our method performs better than typical deep learning and model-based reconstruction strategies for imaging neuronal activity in mammalian brain tissue via LFM, considering reconstruction quality, generalization to functional imaging, and reconstruction speed.

Automated summary

Light Field Microscopy (LFM) is an imaging technique that enables fast dynamics study in biological systems. This work introduces a deep neural network for LFM to image neuronal activity in adverse conditions. The network architecture is based on the sparse nature of neurons and employs a linear convolutional neural network to fit the physics of the acquisition process. With a semi-supervised training framework and physics knowledge, the proposed method outperforms typical deep learning and model-based reconstruction strategies for imaging neuronal activity in mammalian brain tissue via LFM.