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    Method and system for digital staining of microscopy images using deep learning
    35.
    发明授权
    Method and system for digital staining of microscopy images using deep learning 有权

    公开(公告)号：US12300006B2

    公开(公告)日：2025-05-13

    申请号：US17783260

    申请日：2020-12-22

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Yilin Luo ,  Kevin de Haan ,  Yijie Zhang ,  Bijie Bai

    IPC: G06V20/00 ,  G06T7/00 ,  G06V10/82 ,  G06V20/69

    Abstract: A deep learning-based digital/virtual staining method and system enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally/virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital/virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally/virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

    Volumetric microscopy methods and systems using recurrent neural networks
    38.
    发明授权
    Volumetric microscopy methods and systems using recurrent neural networks 有权

    公开(公告)号：US11915360B2

    公开(公告)日：2024-02-27

    申请号：US17505553

    申请日：2021-10-19

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Luzhe Huang

    IPC: G06T15/08 ,  G06F18/214 ,  G06N3/08

    CPC classification number: G06T15/08 ,  G06F18/214 ,  G06N3/08

    Abstract: A deep learning-based volumetric image inference system and method are disclosed that uses 2D images that are sparsely captured by a standard wide-field fluorescence microscope at arbitrary axial positions within the sample volume. Through a recurrent convolutional neural network (RNN) (referred to herein as Recurrent-MZ), 2D fluorescence information from a few axial planes within the sample is explicitly incorporated to digitally reconstruct the sample volume over an extended depth-of-field. Using experiments on C. elegans and nanobead samples, Recurrent-MZ is demonstrated to increase the depth-of-field of a 63×/1.4 NA objective lens by approximately 50-fold, also providing a 30-fold reduction in the number of axial scans required to image the same sample volume. The generalization of this recurrent network for 3D imaging is further demonstrated by showing its resilience to varying imaging conditions, including e.g., different sequences of input images, covering various axial permutations and unknown axial positioning errors.

    PULSE SHAPING USING DIFFRACTIVE NETWORK DEVICE WITH MODULAR DIFFRACTIVE LAYERS
    39.
    发明公开
    PULSE SHAPING USING DIFFRACTIVE NETWORK DEVICE WITH MODULAR DIFFRACTIVE LAYERS 审中-公开

    公开(公告)号：US20230251189A1

    公开(公告)日：2023-08-10

    申请号：US18010207

    申请日：2021-06-28

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Deniz Mengu ,  Yair Rivenson ,  Muhammed Veli

    IPC: G01N21/3586 ,  G02B5/18

    CPC classification number: G01N21/3586 ,  G02B5/1847

    Abstract: A diffractive network is disclosed that utilizes, in some embodiments, diffractive elements, which are used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system. The diffractive network was experimentally shown to generate various different pulses by designing passive diffractive layers that collectively engineer the temporal waveform of an input terahertz pulse. The results constitute the first demonstration of direct pulse shaping in terahertz spectrum, where the amplitude and phase of the input wavelengths are independently controlled through a passive diffractive device, without the need for an external pump. Furthermore, a modular physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing diffractive network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.

    DIFFRACTIVE OPTICAL NETWORK FOR RECONSTRUCTION OF HOLOGRAMS
    40.
    发明申请
    DIFFRACTIVE OPTICAL NETWORK FOR RECONSTRUCTION OF HOLOGRAMS 有权

    公开(公告)号：US20230024787A1

    公开(公告)日：2023-01-26

    申请号：US17865361

    申请日：2022-07-14

    Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

    Inventor： Aydogan Ozcan ,  Md Sadman Sakib Rahman

    IPC: G03H1/08 ,  G02B27/42 ,  G06N3/067

    Abstract: An all-optical hologram reconstruction system and method is disclosed that can instantly retrieve the image of an unknown object from its in-line hologram and eliminate twin-image artifacts without using a digital processor or a computer. Multiple transmissive diffractive layers are trained using deep learning so that the diffracted light from an arbitrary input hologram is processed all-optically to reconstruct the image of an unknown object at the speed of light propagation and without the need for any external power. This passive diffractive optical network, which successfully generalizes to reconstruct in-line holograms of unknown, new objects and exhibits improved diffraction efficiency as well as extended depth-of-field at the hologram recording distance. The system and method can find numerous applications in coherent imaging and holographic display-related applications owing to its major advantages in terms of image reconstruction speed and computer-free operation.

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