公开(公告)号：US09683938B2

公开(公告)日：2017-06-20

申请号：US14906789

申请日：2014-06-20

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Zoltan Gorocs ,  Yuye Ling ,  Meng Dai Yu

IPC: G02B26/08 ,  G01N21/64 ,  G01N33/50 ,  G01N15/14 ,  G01N33/58

CPC classification number: G01N21/6458 ,  G01N15/1434 ,  G01N15/1456 ,  G01N21/6456 ,  G01N33/50 ,  G01N33/582 ,  G01N2201/0612 ,  G01N2201/062 ,  G01N2201/068 ,  G01N2201/101

Abstract: A scanning system for fluorescent imaging includes a sample holder configured to hold a sample therein, the sample holder defining a sample holding region. A scanner head spans the sample holding region and is movable relative to the sample holder. An array of light sources is disposed on an opposing side of the sample holder and is angled relative thereto. Respective controller are operably coupled to the scanner head and the array of light sources, wherein one controller selectively actuates a one or more rows of the array of light sources and another controller controls movement of the scanner head to capture fluorescent light emitted from within the sample holder in response to illumination from the actuated light sources. A filter designed to filter out scattered light from the sample may be interposed between the sample holder and the scanner head.

公开(公告)号：US09588037B2

公开(公告)日：2017-03-07

申请号：US14412984

申请日：2013-07-12

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Ting-Wei Su

IPC: G01N15/14 ,  G06T7/20 ,  G06T19/20 ,  G01B11/24 ,  G01B11/00 ,  G01N15/10

CPC classification number: G01N15/1463 ,  G01B11/00 ,  G01B11/24 ,  G01N2015/1006 ,  G01N2015/1075 ,  G03H1/0443 ,  G03H1/0465 ,  G03H1/0866 ,  G03H2001/0033 ,  G03H2001/0447 ,  G03H2210/30 ,  G03H2222/34 ,  G06T19/20 ,  G06T2200/04 ,  G06T2207/30004

Abstract: A system for three dimensional imaging of motile objects includes an image sensor and a sample holder disposed adjacent to the image sensor. A first illumination source is provided and has a first wavelength and positioned relative to the sample holder at a first location to illuminate the sample. A second illumination source is also provided having a second wavelength, different from the first wavelength, and positioned relative to the sample holder at a second location, different from the first location, to illuminate the sample. The first and second illumination sources are configured to simultaneously, or alternatively, sequentially illuminate the sample contained within the sample holder. Three dimensional positions of the motile objects in each frame are obtained based on digitally reconstructed projection images of the mobile objects obtained from the first and second illumination sources. This positional data is connected for each frame to obtain 3D trajectories of motile objects.

Abstract translation: 用于运动物体的三维成像的系统包括图像传感器和邻近图像传感器设置的样本保持器。 提供第一照明源并且具有第一波长并且在第一位置处相对于样品保持器定位以照亮样品。 还提供第二照明源，其具有不同于第一波长的第二波长，并且在与第一位置不同的第二位置处相对于样品保持器定位以照射样品。 第一和第二照明源被配置为同时或替代地顺序地照射样品保持器中包含的样品。 基于从第一和第二照明光源获得的移动物体的数字重建的投影图像，获得每帧中运动物体的三维位置。 该位置数据被连接到每个帧以获得运动对象的3D轨迹。

公开(公告)号：US20160334614A1

公开(公告)日：2016-11-17

申请号：US15154279

申请日：2016-05-13

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Euan McLeod

IPC: G02B21/36 ,  C23C16/44 ,  G03H1/04 ,  G02B1/04 ,  G03H1/00

CPC classification number: G02B21/365 ,  B82Y20/00 ,  C23C14/12 ,  C23C14/223 ,  C23C14/541 ,  G01N15/1468 ,  G02B21/361 ,  G03H1/0443 ,  G03H2001/0447 ,  G03H2001/0452 ,  G03H2001/046 ,  G03H2222/34 ,  G03H2240/56 ,  Y10S977/881 ,  Y10S977/891

Abstract: A method of forming nanolenses for imaging includes providing an optically transparent substrate having a plurality of particles disposed on one side thereof. The optically transparent substrate is located within a chamber containing therein a reservoir holding a liquid solution. The liquid solution is heated to form a vapor within the chamber, wherein the vapor condenses on the substrate to form nanolenses around the plurality of particles. The particles are then imaged using an imaging device. The imaging device may be located in the same device that contains the reservoir or a separate imaging device.

Abstract translation: 一种形成用于成像的纳米技术的方法包括提供具有设置在其一侧上的多个颗粒的光学透明基板。 光学透明基板位于容纳有液体溶液的储存器的腔室内。 液体溶液被加热以在腔室内形成蒸汽，其中蒸汽在衬底上冷凝以在多个颗粒周围形成纳米通道。 然后使用成像装置对颗粒进行成像。 成像装置可以位于包含储存器或单独的成像装置的相同装置中。

公开(公告)号：US20160327473A1

公开(公告)日：2016-11-10

申请号：US15111472

申请日：2015-01-12

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Qingshan Wei

IPC: G01N21/31 ,  H04N5/232 ,  G06T7/40 ,  H04N5/225 ,  G01N33/18 ,  G06T7/00

CPC classification number: G01N21/3151 ,  G01N33/1813 ,  G01N2015/0693 ,  G01N2201/062 ,  G01N2201/0638 ,  G01N2201/127 ,  G06T7/0012 ,  G06T7/90 ,  G06T2207/30148 ,  H04N5/2251 ,  H04N5/23293

Abstract: The concentration of mercury in a sample is measured by a reader secured to a camera-containing mobile electronic device. The reader has holders for sample and control solutions. First and second light sources emitting light at different colors illuminate the sample and control holders. Each holder contains gold nanoparticles, thymine-rich aptamers, and sodium chloride. The light sources illuminate the sample and control holders. An image is captured of the transmitted light through the sample and control holders, wherein the image comprises two control regions of interest and two sample regions of interest. The device calculates the intensity of the two control regions of interest and the two sample regions of interest and generates intensity ratios for the sample and control, respectively, at each color. The device calculates a normalized color ratio based on the intensity ratios and outputs a concentration of mercury based on the normalized color ratio.

Abstract translation: 样品中汞的浓度通过固定到含摄像机的移动电子设备的读取器来测量。 读者拥有样品和控制解决方案的持有人。 以不同颜色发光的第一和第二光源照射样品和控制器。 每个支架都含有金纳米颗粒，富含胸腺嘧啶的适体和氯化钠。 光源照亮样品和控制夹。 通过样本和控制保持器捕获透射光的图像，其中图像包括感兴趣的两个控制区域和两个感兴趣的样本区域。 该装置计算感兴趣的两个控制区域和两个感兴趣的样本区域的强度，并分别在每个颜色处产生样本和对照的强度比。 该装置基于强度比计算标准化的颜色比，并且基于归一化的颜色比率输出汞的浓度。

公开(公告)号：US20250138296A1

公开(公告)日：2025-05-01

申请号：US18721509

申请日：2023-01-13

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Deniz Mengu

IPC: G02B21/14 ,  G02B27/42 ,  G02B27/52

Abstract: Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of objects. Here, a diffractive QPI network architecture is disclosed that can synthesize the quantitative phase image of an object by converting the input phase information of a scene or object(s) into intensity variations at the output plane. A diffractive QPI network is a specialized all-optical device designed to perform a quantitative phase-to-intensity transformation through passive diffractive/reflective surfaces that are spatially engineered using deep learning and image data. Forming a compact, all-optical network that axially extends only ˜200-300λ (λ=illumination wavelength), this framework replaces traditional QPI systems and related digital computational burdens with a set of passive substrate layers. All-optical diffractive QPI networks can potentially enable power-efficient, high frame-rate and compact phase imaging systems that might be useful for various applications, including, e.g., on-chip microscopy and sensing.

公开(公告)号：US12270068B2

公开(公告)日：2025-04-08

申请号：US17793926

申请日：2021-01-27

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Hatice Ceylan Koydemir ,  Yunzhe Qiu

IPC: C12Q1/04 ,  G02B21/26 ,  G02B21/36 ,  G03H1/00 ,  G06V10/10 ,  G06V10/82 ,  G06V20/69 ,  H04N23/56 ,  H04N23/698

Abstract: A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

公开(公告)号：US20240290473A1

公开(公告)日：2024-08-29

申请号：US18572113

申请日：2022-06-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ,  UNITED STATES GOVERNMENT AS REPRESENTED BY THE DEPARTMENT OF VETERANS AFFAIRS

Inventor： Aydogan Ozcan ,  Jingxi Li ,  Yair Rivenson ,  Xiaoran Zhang ,  Philip O. Scumpia ,  Jason Garfinkel ,  Gennady Rubinstein

IPC: G16H30/40 ,  A61B5/00 ,  G06T7/00 ,  G06T15/08

CPC classification number: G16H30/40 ,  A61B5/0068 ,  A61B5/0071 ,  G06T7/0012 ,  G06T15/08 ,  G06T2207/20084 ,  G06T2207/30088

Abstract: A deep learning-based system and method is provided that uses a convolutional neural network to rapidly transform in vivo reflectance confocal microscopy (RCM) images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of epidermis, dermal-epidermal junction, and superficial dermis layers. The network is trained using ex vivo RCM images of excised unstained tissue and microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. The trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features of traditional histology from the same excised tissue. The system and method enables more rapid diagnosis of malignant skin neoplasms and reduces invasive skin biopsies.

公开(公告)号：US20240288701A1

公开(公告)日：2024-08-29

申请号：US18571653

申请日：2022-06-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yi Luo ,  Ege Cetintas ,  Yair Rivenson

IPC: G02B27/09 ,  G02B5/18 ,  G02B6/42

CPC classification number: G02B27/0944 ,  G02B5/1866 ,  G02B6/4206

Abstract: A computer-free system and method is disclosed that uses an all-optical image reconstruction method to see through random diffusers at the speed of light. Using deep learning, a set of transmissive layers are trained to all-optically reconstruct images of arbitrary objects that are distorted by random phase diffusers. After the training stage, the resulting diffractive layers are fabricated and form a diffractive optical network that is physically positioned between the unknown object and the image plane to all-optically reconstruct the object pattern through an unknown, new phase diffuser. Unlike digital methods, all-optical diffractive reconstructions do not require power except for the illumination light. This diffractive solution to see through diffusive and/or scattering media can be extended to other wavelengths, and can fuel various applications in biomedical imaging, astronomy, atmospheric sciences, oceanography, security, robotics, autonomous vehicles, among many others.

公开(公告)号：US11946854B2

公开(公告)日：2024-04-02

申请号：US17418782

申请日：2019-12-23

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Yichen Wu

IPC: G01N15/14 ,  G01N21/64 ,  G06F18/214 ,  G06N3/08 ,  G06T3/40 ,  G06T3/4046 ,  G06T3/4053 ,  G06T5/00 ,  G06T5/50 ,  G06V10/44 ,  G06V10/77 ,  G06V10/82 ,  G06V20/69

CPC classification number: G01N15/1475 ,  G01N21/6458 ,  G06F18/214 ,  G06N3/08 ,  G06T3/4046 ,  G06T3/4053 ,  G06T5/003 ,  G06T5/50 ,  G06V10/454 ,  G06V10/7715 ,  G06V10/82 ,  G06V20/69 ,  G06T2207/10016 ,  G06T2207/10056 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221

Abstract: A fluorescence microscopy method includes a trained deep neural network. At least one 2D fluorescence microscopy image of a sample is input to the trained deep neural network, wherein the input image(s) is appended with a digital propagation matrix (DPM) that represents, pixel-by-pixel, an axial distance of a user-defined or automatically generated surface within the sample from a plane of the input image. The trained deep neural network outputs fluorescence output image(s) of the sample that is digitally propagated or refocused to the user-defined surface or automatically generated. The method and system cross-connects different imaging modalities, permitting 3D propagation of wide-field fluorescence image(s) to match confocal microscopy images at different sample planes. The method may be used to output a time sequence of images (e.g., time-lapse video) of a 2D or 3D surface within a sample.

公开(公告)号：US11893739B2

公开(公告)日：2024-02-06

申请号：US17041447

申请日：2019-03-29

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Hongda Wang ,  Zhensong Wei

IPC: G06T7/11 ,  G16H70/60 ,  G16H30/20 ,  G16H30/40 ,  G06N3/08 ,  G06F18/214 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06T7/11 ,  G06F18/2155 ,  G06N3/08 ,  G06V10/764 ,  G06V10/82 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

Abstract: A deep learning-based digital staining method and system are disclosed that enables the creation of digitally/virtually-stained microscopic images from label or stain-free samples based on autofluorescence images acquired using a fluorescent microscope. The system and method have particular applicability for the creation of digitally/virtually-stained whole slide images (WSIs) of unlabeled/unstained tissue samples that are analyzes by a histopathologist. The methods bypass the standard histochemical staining process, saving time and cost. This method is based on deep learning, and uses, in one embodiment, a convolutional neural network trained using a generative adversarial network model to transform fluorescence images of an unlabeled sample into an image that is equivalent to the brightfield image of the chemically stained-version of the same sample. This label-free digital staining method eliminates cumbersome and costly histochemical staining procedures and significantly simplifies tissue preparation in pathology and histology fields.