Artificial Intelligence in Label-free Microscopy

Biological Cell Classification by Time Stretch

Nonfiction, Science & Nature, Technology, Electronics, Engineering, Health & Well Being, Medical
Cover of the book Artificial Intelligence in Label-free Microscopy by Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali, Springer International Publishing
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Author: Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali ISBN: 9783319514482
Publisher: Springer International Publishing Publication: April 19, 2017
Imprint: Springer Language: English
Author: Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali
ISBN: 9783319514482
Publisher: Springer International Publishing
Publication: April 19, 2017
Imprint: Springer
Language: English

This book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis.

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This book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis.

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