Artificial Intelligence in Label-free Microscopy Biological Cell Classification by Time Stretch / [electronic resource] :
by Ata Mahjoubfar, Claire Lifan Chen, Bahram Jalali.
- 1st ed. 2017.
- XXXIII, 134 p. 52 illus. in color. online resource.
Introduction -- Background -- Nanometer-resolved imaging vibrometer -- Three-dimensional ultrafast laser scanner -- Label-free High-throughput Phenotypic Screening -- Time Stretch Quantitative Phase Imaging -- Big data acquisition and processing in real-time -- Deep Learning and Classification -- Optical Data Compression in Time Stretch Imaging -- Design of Warped Stretch Transform -- Concluding Remarks and Future Work -- References.
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.
9783319514482
10.1007/978-3-319-51448-2 doi
Biomedical engineering. Electronics. Computer vision. Bioinformatics. Biomedical Engineering and Bioengineering. Electronics and Microelectronics, Instrumentation. Computer Vision. Bioinformatics.