DeepAI AI Chat
Log In Sign Up

Physics-enhanced machine learning for virtual fluorescence microscopy

by   Colin L. Cooke, et al.

This paper introduces a supervised deep-learning network that jointly optimizes the physical setup of an optical microscope to infer fluorescence image information. Specifically, we design a bright-field microscope's illumination module to maximize the performance for inference of fluorescent cellular features from bright-field imagery. We take advantage of the wide degree of flexibility available in illuminating a sample to optimize for programmable patterns of light from a customized LED array, which produce better task-specific performance than standard lighting techniques. We achieve illumination pattern optimization by including a physical model of image formation within the initial layers of a deep convolutional network. Our optimized illumination patterns result in up to a 45 as compared to standard imaging methods, and we additionally explore how the optimized patterns vary as a function of inference task. This work demonstrates the importance of optimizing the process of image capture via programmable optical elements to improve automated analysis, and offers new physical insights into expected performance gains of recent fluorescence image inference work.


page 2

page 3

page 5

page 8

page 11

page 12


Pyramid diffractive optical networks for unidirectional magnification and demagnification

Diffractive deep neural networks (D2NNs) are composed of successive tran...

Convolutional neural networks that teach microscopes how to image

Deep learning algorithms offer a powerful means to automatically analyze...

Multi-element microscope optimization by a learned sensing network with composite physical layers

Standard microscopes offer a variety of settings to help improve the vis...

Noise-Adaptive Intelligent Programmable Meta-Imager

We present an intelligent programmable computational meta-imager that ta...

Towards an Intelligent Microscope: adaptively learned illumination for optimal sample classification

Recent machine learning techniques have dramatically changed how we proc...

Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging

Coded-illumination based reconstruction of Quantitative Phase (QP) is ge...

Programmable 3D snapshot microscopy with Fourier convolutional networks

3D snapshot microscopy enables volumetric imaging as fast as a camera al...