title: High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton.
abstract: Time-stretch imaging has been regarded as an attractive technique for high-throughput imaging flow cytometry primarily owing to its real-time, continuous ultrafast operation. Nevertheless, two key challenges remain: (1) sufficiently high time-stretch image resolution and contrast is needed for visualizing sub-cellular complexity of single cells, and (2) the ability to unravel the heterogeneity and complexity of the highly diverse population of cells - a central problem of single-cell analysis in life sciences - is required. We here demonstrate an optofluidic time-stretch imaging flow cytometer that enables these two features, in the context of high-throughput multi-class (up to 14 classes) phytoplantkton screening and classification. Based on the comprehensive feature extraction and selection procedures, we show that the intracellular texture/morphology, which is revealed by high-resolution time-stretch imaging, plays a critical role of improving the accuracy of phytoplankton classification, as high as 94.7%, based on multi-class support vector machine (SVM). We also demonstrate that high-resolution time-stretch images, which allows exploitation of various feature domains, e.g. Fourier space, enables further sub-population identification - paving the way toward deeper learning and classification based on large-scale single-cell images. Not only applicable to biomedical diagnostic, this work is anticipated to find immediate applications in marine and biofuel research.
authors.forename: Queenie T K
authors.initials: QT
authors.lastname: Lai
authors.forename: Kelvin C M
authors.initials: KC
authors.lastname: Lee
authors.forename: Anson H L
authors.initials: AH
authors.lastname: Tang
authors.forename: Kenneth K Y
authors.initials: KK
authors.lastname: Wong
authors.forename: Hayden K H
authors.initials: HK
authors.lastname: So
authors.forename: Kevin K
authors.initials: KK
authors.lastname: Tsia
mesh_terms: D000465:Algorithms; D005434:Flow Cytometry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D010839:Phytoplankton; D059010:Single-Cell Analysis; D060388:Support Vector Machine