Programmable 3D snapshot microscopy with Fourier convolutional networks

• URL: http://arxiv.org/abs/2104.10611v1
• Date: Wed, 21 Apr 2021 16:09:56 GMT
• Title: Programmable 3D snapshot microscopy with Fourier convolutional networks
• Authors: Diptodip Deb, Zhenfei Jiao, Alex B. Chen, Misha B. Ahrens, Kaspar Podgorski, Srinivas C. Turaga
• Abstract summary: 3D snapshot microscopy enables volumetric imaging as fast as a camera allows by capturing a 3D volume in a single 2D camera image. We introduce a class of global kernel Fourier convolutional neural networks which can efficiently integrate the globally mixed information encoded in a 3D snapshot image.
• Score: 3.2156268397508314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D snapshot microscopy enables volumetric imaging as fast as a camera allows by capturing a 3D volume in a single 2D camera image, and has found a variety of biological applications such as whole brain imaging of fast neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding to preserve as much 3D information as possible is generally unknown and sample-dependent. Highly-programmable optical elements create new possibilities for sample-specific computational optimization of microscope parameters, e.g. tuning the collection of light for a given sample structure, especially using deep learning. This involves a differentiable simulation of light propagation through the programmable microscope and a neural network to reconstruct volumes from the microscope image. We introduce a class of global kernel Fourier convolutional neural networks which can efficiently integrate the globally mixed information encoded in a 3D snapshot image. We show in silico that our proposed global Fourier convolutional networks succeed in large field-of-view volume reconstruction and microscope parameter optimization where traditional networks fail.

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