Tomocube, Inc.

Korea Advanced Institute of Science and Technology

Youngju Jo

YongKeun Park

Geon Yeong Kim

Hyunseok Min

Kihyun Hong

Hyun-Seok Min

Weisun Park

Hyungjoo Cho

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying the predicted type of one or more microorganisms. In one aspect, a system comprises a phase-contrast microscope and a microorganism classification system. The phase-contrast microscope is configured to generate a three-dimensional quantitative phase image of one or more microorganisms. The microorganism classification system is configured to process the three-dimensional quantitative phase image using a neural network to generate a neural network output characterizing the microorganisms, and thereafter identify the predicted type of the microorganisms using the neural network output.

Identifying microorganisms using three-dimensional quantitative phase imaging

Disclosed are a method and apparatus for generating a three-dimensional (3-D) molecular image based on a label-free method using a 3-D refractive index image and deep learning. The apparatus for generating a 3-D molecular image based on a label-free method using a 3-D refractive index image and deep learning may include a 3-D refractive index cell image measurement unit configured to measure a 3-D refractive index image of a cell to be monitored and a 3-D refractive index and fluorescence molecule staining image conversion unit configured to input a measured value of the 3-D refractive index image to a deep learning algorithm and to output a 3-D fluorescence molecule staining cell image of the cell.

Method and apparatus for generating 3-D molecular image based on label-free method using 3-D refractive index image and deep learning

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Daejeon, South Korea

Youngju Jo