University of California

Hatice Ceylan Koydemir

Aydogan Ozcan

Yair Rivenson

Yibo Zhang

Hongda Wang

Derek Tseng

Steve Wei Feng

Bingen Cortazar

Yunzhe Qiu

A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

Systems and methods for the early detection and classification of live microorganisms using time-lapse coherent imaging and deep learning

Systems and methods for detecting motile objects (e.g., parasites) in a fluid sample by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. The imaging platform includes one or more substantially optically transparent sample holders. The imaging platform has a moveable scanning head containing light sources and corresponding image sensor(s) associated with the light source(s). The light source(s) are directed at a respective sample holder containing a sample and the respective image sensor(s) are positioned below a respective sample holder to capture time-varying holographic speckle patterns of the sample contained in the sample holder. The image sensor(s). A computing device is configured to receive time-varying holographic speckle pattern image sequences obtained by the image sensor(s). The computing device generates a 3D contrast map of motile objects within the sample use deep learning-based classifier software to identify the motile objects.

Device and method for motility-based label-free detection of motile objects in a fluid sample

A system for measuring chlorophyll concentration in a leaf sample includes a leaf-holding illuminator device with a main body containing a power source, a plurality of switchable light sources emitting light at different spectra (e.g., red and white light from a broadband light source), and a cap detachably secured to the main body using one or more fastening means. The leaf sample is interposed between the main body and the cap and held in place during imaging. The system includes a mobile electronic device having a camera configured to capture an image of the leaf illuminated by the plurality of switchable light sources, the mobile electronic device having wireless connectivity to a network and an application contained therein configured to transfer the images to a remote sever or computer via the network for data processing. A final chlorophyll index value is calculated based on the transferred images.

Method and device for quantification of plant chlorophyll content

Growing community of inventors

Los Angeles, CA, United States of America

Hatice Ceylan Koydemir