Deep learning extended depth-of-field microscope for fast and slide-free histology
Abstract
Traditional microscopy suffers from a fixed trade-off between depth-of-field (DOF) and spatial resolution—the higher the desired spatial resolution, the narrower the DOF. We present DeepDOF, a computational microscope that allows us to break free from this constraint and achieve >5× larger DOF while retaining cellular-resolution imaging—obviating the need for z-scanning and significantly reducing the time needed for imaging. The key ingredients that allow this advance are 1) an optimized phase mask placed at the microscope aperture; and 2) a deep-learning-based algorithm that turns sensor data into high-resolution, large-DOF images. DeepDOF offers an inexpensive means for fast and slide-free histology, suited for improving tissue sampling during intraoperative assessment and in resource-constrained settings.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 14, 2020
- Source ID
- 10.1073/pnas.2013571117
Entities
People
- Ann M. Gillenwater
- Ashok Veeraraghavan
- Hawraa Badaoui
- Jackson B Coole
- Jacob T Robinson
- Lingbo Jin
- Melody T Tan
- Michelle D. Williams
- Rebecca R. Richards-Kortum
- Xuan Zhao
- Yicheng Wu
- Yubo Tang
Organizations
- National Cancer Institute
- National Institutes of Health
- National Science Foundation
- Rice University
- University of Texas at Austin