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公开(公告)号：US11450062B2
公开(公告)日：2022-09-20
申请号：US16823453
申请日：2020-03-19
Inventor： YongKeun Park , Weisun Park , Youngju Jo , Hyunseok Min , Hyungjoo Cho
Abstract: 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.
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公开(公告)号：US20200081236A1
公开(公告)日：2020-03-12
申请号：US16308329
申请日：2017-06-09
Inventor： YongKeun PARK
Abstract: Presented are a structured illumination microscopy system using a digital micromirror device and a time-complex structured illumination, and an operation method therefor. A structured illumination microscopy system using a digital micromirror device and a time-complex structured illumination according to an embodiment may comprise: a light source; a digital micromirror device (DMD) for receiving light irradiated from the light source, implementing a time-complex structured illumination, and causing a controlled structured illumination to enter a sample; and a fluorescence image measurement unit for extracting a high-resolution 3D fluorescence image of the sample.
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3.发明公开公开(公告)号：US20240331155A1
公开(公告)日：2024-10-03
申请号：US18594475
申请日：2024-03-04
Applicant: Tomocube, Inc.
Inventor： Jaephil DO , Sumin LEE , Hye-Jin KIM
CPC classification number: G06T7/0014 , G01N21/6428 , G06T15/00 , G01N2021/6439 , G06T2200/04 , G06T2207/10064 , G06T2207/20036 , G06T2207/30024 , G06T2210/41
Abstract: Disclosed are a method and apparatus for detecting mycoplasma using the measurement of a 3-D quantitative phase image. The method of detecting mycoplasma may include generating a three-dimensional (3-D) quantitative phase image of a sample by characterizing the sample by making quantitative a phase shift derived from light that passes through the sample, and determining whether the sample has been infected with mycoplasma by analyzing behavioral characteristics of the generated 3-D quantitative phase image.
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公开(公告)号：US20220383562A1
公开(公告)日：2022-12-01
申请号：US17753452
申请日：2021-01-06
Inventor： YongKeun PARK , Donghun RYU , HyunSeok MIN , Dongmin RYU
Abstract: Proposed are a method and device for regularizing rapid three-dimensional tomographic imaging using a machine-learning algorithm. A method for regularizing three-dimensional tomographic imaging using a machine-learning algorithm according to an embodiment comprises the steps of: measuring a three-dimensional tomogram of a cell to acquire a raw tomogram of the cell; acquiring a regularized tomogram by using a regularization algorithm; and learning the relationship between the raw tomogram and the regularized tomogram through machine-learning.
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公开(公告)号：US20210134054A1
公开(公告)日：2021-05-06
申请号：US16823453
申请日：2020-03-19
Inventor： YongKeun Park , Weisun Park , Youngju Jo , Hyunseok Min , Hyungjoo Cho
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20250052560A1
公开(公告)日：2025-02-13
申请号：US18797357
申请日：2024-08-07
Inventor： YongKeun PARK , Herve Jerome Hugonnet
IPC: G01B9/02091 , G01B9/02 , H04N13/207
Abstract: Provided is a high-resolution reflection tomographic imaging system and method. The high-resolution reflection tomographic imaging system of the present disclosure may include an objective lens, a tube lens, a camera, an illumination element configured to introduce temporally coherent and spatially incoherent light, and a semi-reflective surface element configured to split the light into a sample and a reference beam between the tube lens and the camera, such that a sample beam from the sample and the reference beam cause interference for tomographic imaging.
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公开(公告)号：US12141897B2
公开(公告)日：2024-11-12
申请号：US17753452
申请日：2021-01-06
Applicant: TOMOCUBE, INC.
Inventor： YongKeun Park , Donghun Ryu , HyunSeok Min , Dongmin Ryu
Abstract: Proposed are a method and device for regularizing rapid three-dimensional tomographic imaging using a machine-learning algorithm. A method for regularizing three-dimensional tomographic imaging using a machine-learning algorithm according to an embodiment comprises the steps of: measuring a three-dimensional tomogram of a cell to acquire a raw tomogram of the cell; acquiring a regularized tomogram by using a regularization algorithm; and learning the relationship between the raw tomogram and the regularized tomogram through machine-learning.
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公开(公告)号：US12001940B2
公开(公告)日：2024-06-04
申请号：US17951872
申请日：2022-09-23
Applicant: Tomocube, Inc.
Inventor： Kihyun Hong , Hyun-Seok Min , YongKeun Park , Geon Kim , Youngju Jo
CPC classification number: G06N3/045 , G06T7/0012 , G06T2207/10056
Abstract: 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.
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9.发明申请公开(公告)号：US20170357084A1
公开(公告)日：2017-12-14
申请号：US15243265
申请日：2016-08-22
Inventor： YongKeun Park , Seungwoo Shin , Gwang Sik Park
CPC classification number: G02B21/367 , G01N21/4133 , G01N21/45 , G01N21/6458 , G01N21/6486 , G01N2021/1787 , G01N2201/0635 , G02B21/16 , G02B26/0833 , G02B27/46 , G03H1/0005 , G03H2001/005
Abstract: An ultra-high-speed 3D refractive index tomography and structured illumination microscopy system using a wavefront shaper and a method using the same are provided. A method of using an ultra-high-speed 3D refractive index tomography and structured illumination microscopy system that utilizes a wavefront shaper includes adjusting an irradiation angle of a plane wave incident on a sample by using the wavefront shaper, measuring a 2D optical field, which passes through the sample, based on the irradiation angle of the plane wave, and obtaining a 3D refractive index image from information of the measured 2D optical field by using an optical diffraction tomography or a filtered back projection algorithm.
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公开(公告)号：US20230013209A1
公开(公告)日：2023-01-19
申请号：US17951872
申请日：2022-09-23
Inventor： Kihyun Hong , Hyun-Seok Min , YongKeun Park , Geon Kim , Youngju Jo
Abstract: 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.
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