[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Khushi et al., 2017 - Google Patents

MatCol: a tool to measure fluorescence signal colocalisation in biological systems

Khushi et al., 2017

View HTML @Full View
Document ID
17639153534598815522
Author
Khushi M
Napier C
Smyth C
Reddel R
Arthur J
Publication year
Publication venue
Scientific reports

External Links

Snippet

Protein colocalisation is often studied using pixel intensity-based coefficients such as Pearson, Manders, Li or Costes. However, these methods cannot be used to study object- based colocalisations in biological systems. Therefore, a novel method is required to …
Continue reading at www.nature.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
    • G01N27/26—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
    • G01N27/416—Systems
    • G01N27/447—Systems using electrophoresis

Similar Documents

Publication Publication Date Title
Schapiro et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging
Sadanandan et al. Automated training of deep convolutional neural networks for cell segmentation
Rizzardi et al. Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring
Halicek et al. Head and neck cancer detection in digitized whole-slide histology using convolutional neural networks
Eulenberg et al. Reconstructing cell cycle and disease progression using deep learning
Khushi et al. MatCol: a tool to measure fluorescence signal colocalisation in biological systems
Arena et al. Quantitating the cell: turning images into numbers with ImageJ
Levet et al. SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data
Rizk et al. Segmentation and quantification of subcellular structures in fluorescence microscopy images using Squassh
Hejna et al. High accuracy label-free classification of single-cell kinetic states from holographic cytometry of human melanoma cells
Lin et al. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method
Hartig Basic image analysis and manipulation in ImageJ
Shamir et al. IICBU 2008: a proposed benchmark suite for biological image analysis
Giesen et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Chen et al. New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images
Lu et al. IMC-Denoise: a content aware denoising pipeline to enhance Imaging Mass Cytometry
Yapp et al. UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues
Mohapatra et al. Modified Pearson correlation coefficient for two-color imaging in spherocylindrical cells
Haub et al. A model based survey of colour deconvolution in diagnostic brightfield microscopy: Error estimation and spectral consideration
Wen et al. Deep learning–based H-score quantification of immunohistochemistry-stained images
Akbar et al. Comparing computer-generated and pathologist-generated tumour segmentations for immunohistochemical scoring of breast tissue microarrays
Bermudez-Hernandez et al. A method for quantifying molecular interactions using stochastic modelling and super-resolution microscopy
Conway et al. Virtual microscopy as an enabler of automated/quantitative assessment of protein expression in TMAs
Fabrizii et al. Fractal analysis of cervical intraepithelial neoplasia
Schneider et al. Open source bioimage informatics tools for the analysis of DNA damage and associated biomarkers