Systems and methods for two-dimensional fluorescence wave propagation onto surfaces using deep learning

US11946854B2 · kind B2 · utility

Assignee

• The Regents of the University of California · US

Inventors

• Aydogan Ozcan · Los Angeles, US
• Yair Rivenson · Los Angeles, US
• Yichen Wu · Los Angeles, US

Key dates

• Filing date: Dec 23, 2019
• Grant date: Apr 2, 2024
• Expiry date: Aug 13, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06T2207/20221
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

A fluorescence microscopy method includes a trained deep neural network. At least one 2D fluorescence microscopy image of a sample is input to the trained deep neural network, wherein the input image(s) is appended with a digital propagation matrix (DPM) that represents, pixel-by-pixel, an axial distance of a user-defined or automatically generated surface within the sample from a plane of the input image. The trained deep neural network outputs fluorescence output image(s) of the sample that is digitally propagated or refocused to the user-defined surface or automatically generated. The method and system cross-connects different imaging modalities, permitting 3D propagation of wide-field fluorescence image(s) to match confocal microscopy images at different sample planes. The method may be used to output a time sequence of images (e.g., time-lapse video) of a 2D or 3D surface within a sample.