Abstract: Disclosed is a method and device for reconstructing a stress tensor based on a second-order tensor optical response quantity 3D dielectric tensor measurement corresponding to optical anisotropy. A residual stress tensor reconstruction method may include computing a principal axis orientation and a principal component by performing eigenvalue decomposition at each spatial location for an optical anisotropy tensor field; computing the total strain tensor at each spatial location by applying the principal component to an optical anisotropy-strain relationship according to photoelasticity theory; and separating the total strain tensor into an elastic strain component and an eigenstrain component through an inverse operation based on a governing equation according to linear elasticity theory, and calculating the residual stress tensor from the elastic strain component.

Abstract: The present disclosure relates to a high-resolution holotomography imaging apparatus and method based on digital aberration correction. It is configured to detect aberration information for a sample using an optical memory effect, which represents the correlation between the angle of incident light and the angle of transmitted light waves from the sample, and to digitally correct an image of the sample by applying an image correction operation based on the aberration information.

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.

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.

Abstract: The present disclosure provides a system and method for improving image resolution of a three-dimensional (3-D) refractive index microscope based on an artificial intelligence (AI) technology. The present disclosure provides a technology for converting a low-resolution 3-D refractive index microscope image into a high-resolution 3-D refractive index image without physical machine conversion and re-photographing based on AI. That is, the present disclosure applies an AI technology, such as deep learning, in order to train an AI model with a physical correlation between a low-resolution 3-D refractive index microscope image and a high-resolution 3-D refractive index image of various samples, such as a cell and a tissue, and convert a low resolution image into a high resolution image without a change in a physical microscope based on the training. Furthermore, for the training of the AI model, the present disclosure uses physical characteristics of a refractive index image.

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.

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.

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.

Abstract: The present disclosure provides a system and method for improving image resolution of a three-dimensional (3-D) refractive index microscope based on an artificial intelligence (AI) technology. The present disclosure provides a technology for converting a low-resolution 3-D refractive index microscope image into a high-resolution 3-D refractive index image without physical machine conversion and re-photographing based on AI. That is, the present disclosure applies an AI technology, such as deep learning, in order to train an AI model with a physical correlation between a low-resolution 3-D refractive index microscope image and a high-resolution 3-D refractive index image of various samples, such as a cell and a tissue, and convert a low resolution image into a high resolution image without a change in a physical microscope based on the training. Furthermore, for the training of the AI model, the present disclosure uses physical characteristics of a refractive index image.

Abstract: A method and apparatus for measuring a 3-D refractive index tensor are presented. The method for measuring a 3-D refractive index tensor according to an embodiment comprises the steps of: controlling incident light of a plane wave with respect to at least one angle and polarization; and measuring, in a polarization-dependent manner, the 2-D diffracted light of a specimen with respect to the incident light incident at the at least one angle and polarization, wherein the birefringence value and the 3-D structure of an alignment direction of molecules in the specimen having birefringence may be measured.

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.

Abstract: Proposed are a three-dimensional (3D) optical tomography method and apparatus using a partially coherent light and a multi-illumination pattern. The 3D optical diffraction tomography method based on low coherence light and a multi-illumination pattern using a 3D optical diffraction tomography apparatus may include making light incident on a sample using a plurality of patterns, measuring, by an image measurement unit, different locations at different depth locations of the sample and measuring two-dimensional (2D) images of the sample, and reconstructing 3D refractive index information of the sample based on the different patterns and the 2D images obtained at the different depth locations.

Abstract: A non-label diagnosis apparatus for a hematologic malignancy may include a 3-D refractive index cell imaging unit configured to generate a 3-D refractive index slide image of a blood smear specimen by capturing a 3-D refractive index image in the form of the blood smear specimen in which blood (including a bone-marrow or other body fluids) of a patient has been smeared on a slide glass, an ROI detection unit configured to sample a suspected cell segment in the blood smear specimen based on the 3-D refractive index slide image and to determine, as ROI patches, cells determined as abnormal cells, and a diagnosis unit configured to determine a sub-classification of a cancer cell corresponding to each of the ROI patches using a cancer cell sub-classification determination model constructed based on a deep learning algorithm and to generate hematologic malignancy diagnosis results by gathering sub-classification results of the ROI patches.

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.

Abstract: A non-label diagnosis apparatus for a hematologic malignancy may include a 3-D refractive index cell imaging unit configured to generate a 3-D refractive index slide image of a blood smear specimen by capturing a 3-D refractive index image in the form of the blood smear specimen in which blood (including a bone-marrow or other body fluids) of a patent has been smeared on a slide glass, an ROI detection unit configured to sample a suspected cell segment in the blood smear specimen based on the 3-D refractive index slide image and to determine, as ROI patches, cells determined as abnormal cells, and a diagnosis unit configured to determine a sub-classification of a cancer cell corresponding to each of the ROI patches using a cancer cell sub-classification determination model constructed based on a deep learning algorithm and to generate hematologic malignancy diagnosis results by gathering sub-classification results of the ROI patches.

Abstract: A method and apparatus for measuring 3D refractive-index tomograms using a wavefront shaper in ultra-high speed and high precision is provided. The method includes the steps of modifying at least one of an illumination angle and a wavefront pattern of an incident ray through the wavefront shaper and leading the modified incident ray to a sample, measuring a 2D optical field, which passes through the sample, through an interferometry along at least one or more of the incident rays, and obtaining 3D refractive-index tomograms through measured information of the 2D optical field.

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.