Invention Grant
- Patent Title: Deep learning-enabled portable imaging flow cytometer for label-free analysis of water samples
-
Application No.: US15734225Application Date: 2019-06-04
-
Publication No.: US11501544B2Publication Date: 2022-11-15
- Inventor: Aydogan Ozcan , Zoltan Gorocs
- Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Applicant Address: US CA Oakland
- Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee Address: US CA Oakland
- Agency: Vista IP Law Group LLP
- International Application: PCT/US2019/035376 WO 20190604
- International Announcement: WO2019/236569 WO 20191212
- Main IPC: G06V20/69
- IPC: G06V20/69 ; G06T7/215 ; G06T7/254 ; B01L3/00 ; G01N15/14 ; G03H1/00 ; G03H1/04 ; G06K9/62 ; G06T7/00 ; H04N5/225 ; G01N15/10

Abstract:
An imaging flow cytometer device includes a housing holding a multi-color illumination source configured for pulsed or continuous wave operation. A microfluidic channel is disposed in the housing and is fluidically coupled to a source of fluid containing objects that flow through the microfluidic channel. A color image sensor is disposed adjacent to the microfluidic channel and receives light from the illumination source that passes through the microfluidic channel. The image sensor captures image frames containing raw hologram images of the moving objects passing through the microfluidic channel. The image frames are subject to image processing to reconstruct phase and/or intensity images of the moving objects for each color. The reconstructed phase and/or intensity images are then input to a trained deep neural network that outputs a phase recovered image of the moving objects. The trained deep neural network may also be trained to classify object types.
Public/Granted literature
- US20210209337A1 DEEP LEARNING-ENABLED PORTABLE IMAGING FLOW CYTOMETER FOR LABEL-FREE ANALYSIS OF WATER SAMPLES Public/Granted day:2021-07-08
Information query