公开(公告)号：US09903804B2

公开(公告)日：2018-02-27

申请号：US15016217

申请日：2016-02-04

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Bahram Jalali ,  Ata Mahjoubfar

IPC: G01N21/00 ,  G01N15/14 ,  G01B9/02 ,  G06K9/00 ,  G06K9/46 ,  G06T7/00 ,  G06T11/60 ,  G01N15/00 ,  G01N15/10

CPC classification number: G01N15/1434 ,  G01B9/02043 ,  G01N15/1429 ,  G01N15/1459 ,  G01N2015/0065 ,  G01N2015/1006 ,  G01N2015/1087 ,  G01N2015/144 ,  G01N2015/1454 ,  G06K9/00147 ,  G06K9/46 ,  G06T7/0012 ,  G06T11/60 ,  G06T2207/10056 ,  G06T2207/30024 ,  G06T2207/30242

Abstract: A label-free imaging-based flow cytometer that measures size and cell protein concentration simultaneously is disclosed. Cell protein concentration adds a parameter to cell classification that improves the specificity and sensitivity of flow cytometers without the requirement of cell labeling. The system uses coherent dispersive Fourier transform to perform phase imaging at flow speeds as high as a few meters per second. To retrieve cell information in real-time, an analog signal processing system based on quadrature phase demodulation is described.

公开(公告)号：US10593039B2

公开(公告)日：2020-03-17

申请号：US15928992

申请日：2018-03-22

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Bahram Jalali ,  Ata Mahjoubfar ,  Lifan Chen

IPC: G06K9/00 ,  G06T7/00 ,  G16B40/00 ,  G06N3/08 ,  G01N15/14 ,  G06K9/46 ,  G06N3/04 ,  G01N15/10

Abstract: A method and apparatus for using deep learning in label-free cell classification and machine vision extraction of particles. A time stretch quantitative phase imaging (TS-QPI) system is described which provides high-throughput quantitative imaging, and utilizing photonic time stretching. In at least one embodiment, TS-QPI is integrated with deep learning to achieve record high accuracies in label-free cell classification. The system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. The system is particularly well suited for data-driven phenotypic diagnosis and improved understanding of heterogeneous gene expression in cells.

公开(公告)号：US20180286038A1

公开(公告)日：2018-10-04

申请号：US15928992

申请日：2018-03-22

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Bahram Jalali ,  Ata Mahjoubfar ,  Lifan Chen

IPC: G06T7/00 ,  G06F19/24 ,  G06N3/08 ,  G01N15/14 ,  G06N3/04

CPC classification number: G06T7/0012 ,  G01N15/1429 ,  G01N15/1434 ,  G01N15/147 ,  G01N15/1475 ,  G01N2015/1006 ,  G01N2015/144 ,  G01N2015/1445 ,  G01N2015/1454 ,  G01N2015/149 ,  G06K9/00127 ,  G06K9/0014 ,  G06K9/00147 ,  G06K9/4628 ,  G06N3/04 ,  G06N3/086 ,  G16B40/00

Abstract: A method and apparatus for using deep learning in label-free cell classification and machine vision extraction of particles. A time stretch quantitative phase imaging (TS-QPI) system is described which provides high-throughput quantitative imaging, and utilizing photonic time stretching. In at least one embodiment, TS-QPI is integrated with deep learning to achieve record high accuracies in label-free cell classification. The system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. The system is particularly well suited for data-driven phenotypic diagnosis and improved understanding of heterogeneous gene expression in cells.

公开(公告)号：US20160223453A1

公开(公告)日：2016-08-04

申请号：US15016217

申请日：2016-02-04

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Bahram Jalali ,  Ata Mahjoubfar

IPC: G01N15/14 ,  G06T7/00 ,  G06K9/46 ,  G06K9/00 ,  G01B9/02 ,  G06T11/60

CPC classification number: G01N15/1434 ,  G01B9/02043 ,  G01N15/1429 ,  G01N15/1459 ,  G01N2015/0065 ,  G01N2015/1006 ,  G01N2015/1087 ,  G01N2015/144 ,  G01N2015/1454 ,  G06K9/00147 ,  G06K9/46 ,  G06T7/0012 ,  G06T11/60 ,  G06T2207/10056 ,  G06T2207/30024 ,  G06T2207/30242

Abstract: A label-free imaging-based flow cytometer that measures size and cell protein concentration simultaneously is disclosed. Cell protein concentration adds a parameter to cell classification that improves the specificity and sensitivity of flow cytometers without the requirement of cell labeling. The system uses coherent dispersive Fourier transform to perform phase imaging at flow speeds as high as a few meters per second. To retrieve cell information in real-time, an analog signal processing system based on quadrature phase demodulation is described.

Abstract translation: 公开了一种同时测量大小和细胞蛋白浓度的无标记成像的流式细胞仪。 细胞蛋白浓度为细胞分类提供参数，提高流式细胞仪的特异性和灵敏度，无需细胞标记。 该系统使用相干色散傅里叶变换来以高达几米每秒的流速执行相位成像。 为了实时检索信元信息，描述了基于正交相位解调的模拟信号处理系统。