Artificial Intelligence in Label-free Microscopy: Biological Cell Classification by Time Stretch

by Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali

Overview

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.

Product Details

ISBN-13:	9783319514482
Publisher:	Springer-Verlag New York, LLC
Publication date:	04/19/2017
Sold by:	Barnes & Noble
Format:	eBook
Pages:	134
File size:	3 MB

About the Author

Ata Mahjoubfar is a postdoctoral scholar in the Department of Electrical Engineering and California NanoSystems Institute at University of California Los Angeles. He received his Bachelor's and Master's degrees from University of Tehran, Iran in 2006 and 2008, respectively, and his Ph.D. degree from University of California, Los Angeles (UCLA) in 2014. He was the cofounder of OSA/SPIE student chapter at UCLA and its president in 2012. He is the author of more than 35 peer-reviewed publications, and he holds two international patents. His research interests include artificial intelligence, machine vision and learning, imaging and visualization, ultrafast data acquisition and analytics, biomedical technology, and financial engineering.
Claire Lifan Chen is a senior application engineer at Lumentum Operations LLC. She received her Ph.D. degree in Electrical Engineering and M.Sc. degree in Bioengineering at University of California, Los Angeles in 2015 and 2012, respectively. She received her B.Sc. degree in Optics Science and Engineering from Fudan University, China in 2010. Dr. Chen was a member of California NanoSystems Institute from 2013 to 2015 and the president of OSA/SPIE student chapter at UCLA in 2015. She has authored and co-authored 14 peer-reviewed publications and 2 patents. Her research interests include machine learning, data acquisition and analytics, image processing, and high-throughput imaging with applications in biomedical and information technologies.
Bahram Jalali is the Northrop-Grumman Endowed Chair and Professor of Electrical Engineering at UCLA with joint appointments in Biomedical Engineering, California NanoSystems Institute (CNSI) and Department of Surgery at the UCLA School of Medicine. He is the inventor of the Photonic Time Stretch, a measurement technique that has led to discoveries of new scientific phenomena and to technological inventions. He received his Ph.D. in Applied Physics from Columbia University in 1989 and was with Bell Laboratories in Murray Hill, New Jersey until 2002 before joining UCLA. He is a Fellow of IEEE, the Optical Society of America (OSA), the American Physical Society (APS) and SPIE. He is the recipient of the R.W. Wood Prize from Optical Society of America for the invention and demonstration of the first Silicon Laser, the Aron Kressel Award of the IEEE Photonics Society, the Achievement Medal from IET for his contributions to field of instrumentation for cancer detection, and the Distinguished Engineering Achievement Award from the Engineers Council. He was the founder and CEO of Cognet, a CMOS fiber optics company that was acquired by Intel in 2001. In 2005 he was elected into the Scientific American Top 50, and received the BrideGate 20 Award in 2001 for his entrepreneurial accomplishments and contributions to Southern California economy.

Table of Contents

I Time stretch imaging1 Introduction2 Background2.1 Time Stretch Imaging2.2 Cell Classification Using Time Stretch Imaging2.3 Label-free Phenotypic Screening2.4 Warped Time Stretch for Data CompressionII Inspection and vision3 Nanometer-resolved imaging vibrometer3.1 Introduction3.2 Experimental demonstration3.3 Theoretical study of the vibrometer performance3.4 Experimental results3.5 Conclusion4 Three-dimensional ultrafast laser scanner4.1 Introduction4.2 Principle of hybrid dispersion laser scanner4.3 Applications of hybrid dispersion laser scannerIII Biomedical applications5 Label-free High-throughput Phenotypic Screening5.1 Introduction5.2 Experimental Setup5.3 Results and Discussion5.4 Conclusion6 Time Stretch Quantitative Phase Imaging6.1 Background6.2 Time Stretch Quantitative Phase Imaging6.2.1 Overview6.2.2 Imaging system6.2.3 System performance and resolvable points6.2.4 Microuidic channel design and fabrication6.2.5 Coherent Detection and Phase Extraction6.2.6 Cell Transmittance Extraction6.2.7 Image Reconstruction6.3 Image Processing Pipeline6.3.1 Feature Extraction6.3.2 Multivariate Features Enabled by Sensor Fusion6.3.3 System Calibration6.4 ConclusionIV Big data and artifitial intelligence7 Big data acquisition and processing in real-time7.1 Introduction7.2 Technical description of the acquisition system7.3 Big data acquisition results7.4 Conclusion8 Deep Learning and Classification8.1 Background8.2 Machine Learning8.3 Applications8.3.1 Blood Screening: Demonstration in Classification of OT-II and SW-480 Cells8.3.2 Biofuel: Demonstration in Algae Lipid Content Classification8.4 Further Discussions in Machine Learning8.4.1 Learning Curves8.4.2 Principal Component Analysis (PCA)8.4.3 Cross Validation8.4.4 Computation Time8.4.5 Data Cleaning8.5 ConclusionV Data compression9 Optical Data Compression in Time Stretch Imaging9.1 Background9.2 Warped Stretch Imaging9.3 Optical Image Compression9.4 Experimental Design and Results9.5 Conclusion10 Design of Warped Stretch Transform10.1 Overview10.2 Kernel Design10.2.1 Spectral Resolution10.2.2 Group Delay Profile Design10.2.3 Simulation Model10.2.4 Spectrograms10.3 Discussion10.4 Conclusion11 Concluding Remarks and Future WorkReferences