Model-inspired Deep Learning for Light-Field Microscopy with Application to Neuron Localization
Year: 2021
Type: preprint
Abstract: Light-field microscopes are able to capture spatial and angular information of incident light rays. This allows reconstructing 3D locations of neurons from a single snap-shot.In this work, we propose a model-inspired deep learning approach to perform fast and robust 3D localization of sources using light-field microscopy images. This is achieved by developing a deep network that efficiently solves a convolutional sparse coding (CSC) problem to map Epipolar Plane Images (EPI) to corresponding sparse codes. The network architecture is designed systematically by unrolling the convolutional Iterative Shrinkage and Thresholding Algorithm (ISTA) while the network parameters are learned from a training dataset. Such principled design enables the deep network to leverage both domain knowledge implied in the model, as well as new parameters learned from the data, thereby combining advantages of model-based and learning-based methods. Practical experiments on localization of mammalian neurons from light-fields show that the proposed approach simultaneously provides enhanced performance, interpretability and efficiency.
Source: arXiv (Cornell University)
Institution: Imperial College London
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Holographic Induction of Neural Circuit Plasticity, EPSRC Centre for Doctoral Training in Neurotechnology for Life and Health, Two-photon Light Field with Neuro-active Sensing for Fast Volumetric Neural Microcircuit Readout, Holographic Induction of Neural Circuit Plasticity, G4485320506, G8017067339
OpenAlex ID: W3135624464