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1. WO2020139835 - SYSTEMS AND METHODS FOR TWO-DIMENSIONAL FLUORESCENCE WAVE PROPAGATION ONTO SURFACES USING DEEP LEARNING

Publication Number WO/2020/139835
Publication Date 02.07.2020
International Application No. PCT/US2019/068347
International Filing Date 23.12.2019
IPC
G01N 15/14 2006.01
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
10Investigating individual particles
14Electro-optical investigation
G06F 19/24 2011.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
19Digital computing or data processing equipment or methods, specially adapted for specific applications
10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
24for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
G06K 9/00 2006.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06T 7/00 2017.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
CPC
G01N 15/1475
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
10Investigating individual particles
14Electro-optical investigation, e.g. flow cytometers
1468with spatial resolution of the texture or inner structure of the particle
1475using image analysis for extracting features of the particle
G01N 2015/1006
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
10Investigating individual particles
1006for cytology
G01N 2015/1488
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
15Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
10Investigating individual particles
14Electro-optical investigation, e.g. flow cytometers
1488Methods for deciding
G06K 9/00134
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
00134Acquisition, e.g. centering the image field
G06K 9/4628
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
46Extraction of features or characteristics of the image
4604Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
4609by matching or filtering
4619Biologically-inspired filters, e.g. receptive fields
4623with interaction between the responses of different filters
4628Integrating the filters into a hierarchical structure
G06K 9/6232
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
62Methods or arrangements for recognition using electronic means
6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
Applicants
  • THE REGENTS OF THE UNIVERSITY OF CALIFORNIA [US]/[US]
Inventors
  • OZCAN, Aydogan
  • RIVENSON, Yair
  • WU, Yichen
Agents
  • DAVIDSON, Michael S.
Priority Data
62/785,01226.12.2018US
62/912,53708.10.2019US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) SYSTEMS AND METHODS FOR TWO-DIMENSIONAL FLUORESCENCE WAVE PROPAGATION ONTO SURFACES USING DEEP LEARNING
(FR) SYSTÈMES ET PROCÉDÉS DE PROPAGATION BIDIMENSIONNELLE D'ONDES DE FLUORESCENCE SUR DES SURFACES À L'AIDE D'UN APPRENTISSAGE PROFOND
Abstract
(EN)
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.
(FR)
L'invention concerne un procédé de microscopie à fluorescence comprenant un réseau neuronal profond entraîné. Au moins une image de microscopie à fluorescence 2D d'un échantillon est entrée dans le réseau neuronal profond entraîné, ladite image d'entrée étant annexée à une matrice de propagation numérique (DPM) qui représente, pixel par pixel, une distance axiale d'une surface définie par l'utilisateur ou automatiquement générée à l'intérieur de l'échantillon à partir d'un plan de l'image d'entrée. Le réseau neuronal profond entraîné émet en sortie une ou plusieurs images de sortie de fluorescence de l'échantillon propagées ou refocalisées numériquement vers la surface définie par l'utilisateur ou générée automatiquement. Le procédé et le système croisent les différentes modalités d'imagerie, ce qui permet à la propagation 3D d'une ou plusieurs images de fluorescence à grand champ de correspondre à des images de microscopie confocale à différents plans d'échantillon. Le procédé peut être utilisé pour émettre en sortie une séquence temporelle d'images (par exemple, une vidéo à intervalles de temps) d'une surface 2D ou 3D à l'intérieur d'un échantillon.
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