Designing Microscopes with Deep Learning

We are attempting to design a new breed of microscopes which are geared towards performing a task in the most efficient way possible. Ever since computational resources have become widespread, we have increasingly used machine learning to draw inference from the images taken from microscopes. We take this to next step and use deep learning to inform us about the optimal design of the optical hardware of the microscopes.

Computational Optics Lab

This concept was developed in the Computational Optics Lab at Duke University. The lab’s research falls at the intersection of biomedical imaging, biophotonics and algorithm design. We develop new microscopes, cameras and computer algorithms for better biomedical images. The lab is directed by Dr. Roarke Horstmeyer, who is a new Assistant Professor in the Biomedical Engineering Department at Duke Univeristy.


This concept is also explored in a graduate Duke BME course, Machine Learning for Imaging. This class is an overview of machine learning and imaging science, with a focus on the intersection of the two fields. Students will create a machine learning project with an imaging componenet at the end of the course. More information on the course and the materials can be found at the course website and past student projets are here.