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Proceedings Paper

Label-free multi-class classification of phytoplankton based on quantitative phase time-stretch imaging (Conference Presentation)
Author(s): Queenie Tsz Kwan K. Lai; Kelvin C. M. Lee; Kenneth K. Y. Wong; Kevin K. Tsia

Paper Abstract

Phytoplankton is highly diversified in species, differing in size, geometries, morphology and biochemical composition. Such diversity plays a critical role in the atmospheric carbon cycle and marine ecosystem. Large-scale quantitation and classification of phytoplankton with taxonomic information is thus of significance in environmental monitoring and even biofuel production. To this end, we report a high-throughput, label-free imaging flow cytometer (>10,000 cells/sec) based on quantitative phase time stretch imaging flow cytometry, combined with a supervised learning strategy for multi-class classification of phytoplankton (13 classes). This is in contrast to the previous demonstrations on integrating machine learning with time-stretch imaging which achieve high-accuracy binary (two-class) image-based classification. We leverage interferometry-free quantitative phase time-stretch imaging which favors generation of high-resolution and high-contrast single-cell (phytoplankton) images with both quantitative phase and amplitude contrasts, we can extract a catalogue of 109 image-content-rich features (44 from the amplitude image and 65 from the phase image), not only limited to sizes, shapes, but also sub-cellular morphology, e.g. local dry mass density statistics. By using the random forest algorithm for feature ranking, we select 30 most significant features for a multi-class SVM model and achieve a high classification accuracy (> 95%) across 13 classes of phytoplankton. Almost 50% of these selected features are derived from the quantitative phase and play an important role in classifying morphologically similar species, e.g. Thalassiosira versus Prorocentrum; Chaetoceros gracilis versus Merismopedia – demonstrating the classification power of this quantitative phase time-stretch imaging flow cytometer required for large-scale high-content screening and analysis.

Paper Details

Date Published: 15 March 2018
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Proc. SPIE 10505, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, 105050B (15 March 2018); doi: 10.1117/12.2291964
Show Author Affiliations
Queenie Tsz Kwan K. Lai, The Univ. of Hong Kong (Hong Kong, China)
Kelvin C. M. Lee, The Univ. of Hong Kong (Hong Kong, China)
Kenneth K. Y. Wong, The Univ. of Hong Kong (Hong Kong, China)
Kevin K. Tsia, The Univ. of Hong Kong (Hong Kong, China)


Published in SPIE Proceedings Vol. 10505:
High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)

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