Artificial intelligence in label-free microscopy : biological cell classification by time stretch

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-...

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Bibliographic Details
Main Authors: Mahjoubfar, Ata, (Author), Chen, Claire Lifan, (Author), Jalali, B., (Author)
Format: eBook
Language: English
Published: Cham, Switzerland : Springer, 2017.
Subjects:
Artificial intelligence > Biological applications.
Microscopy.
elektronické knihy
electronic books
ISBN: 9783319514482
9783319514475
Physical Description: 1 online resource (xxxiii, 134 pages) : color illustrations

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100 1 |a Mahjoubfar, Ata,  |e author. 
245 1 0 |a Artificial intelligence in label-free microscopy :  |b biological cell classification by time stretch /  |c Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali. 
264 1 |a Cham, Switzerland :  |b Springer,  |c 2017. 
300 |a 1 online resource (xxxiii, 134 pages) :  |b color illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a Preface; Acknowledgements; Contents; List of Figures; List of Tables; Part I Time Stretch Imaging; 1 Introduction; 2 Time Stretch; 2.1 Time Stretch Imaging; 2.2 Cell Classification Using Time Stretch Imaging; 2.3 Label-Free Phenotypic Screening; 2.4 Warped Time Stretch for Data Compression; Part II Inspection and Vision; 3 Nanometer-Resolved Imaging Vibrometer; 3.1 Introduction; 3.2 Experimental Demonstration; 3.3 Theoretical Study of the Vibrometer Performance; 3.4 Experimental Results; 3.5 Conclusion; 4 Three-Dimensional Ultrafast Laser Scanner; 4.1 Introduction. 
505 8 |a 4.2 Principle of Hybrid Dispersion Laser Scanner4.3 Applications of Hybrid Dispersion Laser Scanner; Part III Biomedical Applications; 5 Label-Free High-Throughput Phenotypic Screening; 5.1 Introduction; 5.2 Experimental Setup; 5.3 Results and Discussion; 5.4 Conclusion; 6 Time Stretch Quantitative Phase Imaging; 6.1 Background; 6.2 Time Stretch Quantitative Phase Imaging; 6.2.1 Overview; 6.2.2 Imaging System; 6.2.3 System Performance and Resolvable Points; 6.2.4 Microfluidic Channel Design and Fabrication; 6.2.5 Coherent Detection and Phase Extraction; 6.2.6 Cell Transmittance Extraction. 
505 8 |a 6.2.7 Image Reconstruction6.3 Image Processing Pipeline; 6.3.1 Feature Extraction; 6.3.2 Multivariate Features Enabled by Sensor Fusion; 6.3.3 System Calibration; 6.4 Conclusion; Part IV Big Data and Artificial Intelligence; 7 Big Data Acquisition and Processing in Real-Time; 7.1 Introduction; 7.2 Technical Description of the Acquisition System; 7.3 Big Data Acquisition Results; 7.4 Conclusion; 8 Deep Learning and Classification; 8.1 Background; 8.2 Machine Learning; 8.3 Applications; 8.3.1 Blood Screening: Demonstration in Classification of OT-II and SW-480 Cells. 
505 8 |a 8.3.2 Biofuel: Demonstration in Algae Lipid Content Classification8.4 Further Discussions in Machine Learning; 8.4.1 Learning Curves; 8.4.2 Principal Component Analysis (PCA); 8.4.3 Cross Validation; 8.4.4 Computation Time; 8.4.5 Data Cleaning; 8.5 Conclusion; Part V Data Compression; 9 Optical Data Compression in Time Stretch Imaging; 9.1 Background; 9.2 Warped Stretch Imaging; 9.3 Optical Image Compression; 9.4 Experimental Design and Results; 9.5 Conclusion; 10 Design of Warped Stretch Transform; 10.1 Overview; 10.2 Kernel Design; 10.2.1 Spectral Resolution. 
505 8 |a 10.2.2 Group Delay Profile Design10.2.3 Simulation Model; 10.2.4 Spectrograms; 10.3 Discussion; 10.4 Conclusion; 11 Concluding Remarks and Future Work; References; Index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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." Demonstrates how machine learning is used in high-speed microscopy imaging to facilitate medical diagnosis; " Provides a systematic and comprehensive illustration of time stretch technology; " Enables multidisciplinary application, including industrial, biomedical, and artificial intelligence. 
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650 0 |a Artificial intelligence  |x Biological applications. 
650 0 |a Microscopy. 
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700 1 |a Chen, Claire Lifan,  |e author. 
700 1 |a Jalali, B.,  |e author. 
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