Method and apparatus for rapid diagnosis of hematologic malignancy using 3D quantitative phase imaging and deep learning

    公开(公告)号:US11410304B2

    公开(公告)日:2022-08-09

    申请号:US16900364

    申请日:2020-06-12

    Applicant: TOMOCUBE, INC.

    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.

    SYSTEM AND METHOD FOR IMPROVING IMAGE RESOLUTION OF 3-D REFRACTIVE INDEX MICROSCOPE BASED ON AI TECHNOLOGY

    公开(公告)号:US20230410256A1

    公开(公告)日:2023-12-21

    申请号:US17818987

    申请日:2022-08-11

    Applicant: Tomocube, Inc.

    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.

    METHOD AND APPARATUS FOR MEASURING THREE-DIMENSIONAL REFRACTIVE INDEX TENSOR

    公开(公告)号:US20220404267A1

    公开(公告)日:2022-12-22

    申请号:US17753479

    申请日:2021-01-07

    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.

    Three-dimensional optical tomography method and apparatus using partially coherent light and multi-illumination pattern

    公开(公告)号:US11408725B2

    公开(公告)日:2022-08-09

    申请号:US17024303

    申请日:2020-09-17

    Applicant: Tomocube, Inc.

    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.

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