FRAMEWORK FOR IMAGE BASED UNSUPERVISED CELL CLUSTERING AND SORTING

A framework that includes a feature extractor and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple ce...

Full description

Saved in:
Bibliographic Details
Main Authors Chiang, Su-Hui, Liu, Ming-Chang
Format Patent
LanguageEnglish
Published 19.05.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES
MEASURING
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
TESTING
Online AccessGet full text

Cover

More Information
Summary:A framework that includes a feature extractor and a cluster component for clustering is described herein. The framework supports (1) offline image-based unsupervised clustering that replaces time-consuming manual gating; (2) online image-based single cell sorting. During training, one or multiple cell image datasets with or without ground truth are used to train feature extractor, which is based on a neural network including several convolutional layers. Once trained, the feature extractor is used to extract features of cell images for unsupervised cell clustering and sorting. In addition, additional datasets may be used to further refine the feature extractor after it has been trained.
Bibliography:Application Number: US202117222131