Pure mathematics

Biomedical engineering

Bright-field microscopy

Materials science

Microscope

Microscopy

Aperture (computer memory)

Field (mathematics)

Computer science

Acoustics

Filter (signal processing)

Resolution (logic)

Image resolution

Physics

Mathematics

Optics

Depth of field

Sampling (signal processing)

Optical microscope

Fluorescence

Artificial intelligence

Medicine

Scanning electron microscope

Deep learning

Fluorescence microscope

Optical sectioning

Histology

Computer vision

Pathology

Proceedings of the National Academy of Sciences

Significance 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 &gt;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.

Deep learning extended depth-of-field microscope for fast and slide-free histology