Artificial Intelligence in Digital Holographic Imaging: Technical Basis and Biomedical Applications

Artificial Intelligence in Digital Holographic Imaging Technical Basis and Biomedical Applications An eye-opening discussion of 3D optical sensing, imaging, analysis, and pattern recognition Artificial intelligence (AI) has made great progress in recent years. Digital holographic imaging has recently emerged as a powerful new technique well suited to explore cell structure and dynamics with a nanometric axial sensitivity and the ability to identify new cellular biomarkers. By combining digital holography with AI technology, including recent deep learning approaches, this system can achieve a record-high accuracy in non-invasive, label-free cellular phenotypic screening. It opens up a new path to data-driven diagnosis. Artificial Intelligence in Digital Holographic Imaging introduces key concepts and algorithms of AI to show how to build intelligent holographic imaging systems drawing on techniques from artificial neural networks, convolutional neural networks, and generative adversarial network. Readers will be able to gain an understanding of the basics for implementing AI in holographic imaging system designs and connecting practical biomedical questions that arise from the use of digital holography with various AI algorithms in intelligence models. What’s Inside Introductory background on digital holography Key concepts of digital holographic imaging Deep-learning techniques for holographic imaging AI techniques in holographic image analysis Holographic image-classification models Automated phenotypic analysis of live cells For readers with various backgrounds, this book provides a detailed discussion of the use of intelligent holographic imaging system in biomedical fields with great potential for biomedical application.

Table of Contents:
Part I. Digital Holographic Microscopy (DHM) 1. Introduction References 2. Coherent optical imaging 2.1 Monochromatic fields and irradiance 2.2 Analytic expression for Fresnel diffraction 2.3 Transmittance function of lens 2.4 Geometrical imaging concepts 2.5 Coherent imaging theory References 3. Lateral and depth resolutions 3.1 Lateral resolution 3.2 Depth (or axial) resolution References 4. Phase unwrapping 4.1 Branch cuts 4.2 Quality-guided path-following algorithms References 5. Off-axis digital holographic microscopy 5.1 Off-axisdigital holographic microscopy designs 5.2 Digital hologram reconstruction References 6. Gabor digital holographic microscopy 6.1 Introduction 6.2 Methodology References   Part II. Deep Learning in DHM Systems 7. Introduction  References 8. No-search focus prediction in DHM with deep learning 8.1 Introduction 8.2 Materials and methods 8.3 Experimental results 8.4 Conclusions References 9. Automated phase unwrapping in DHM with deep learning 9.1 Introduction 9.2 Deep learning model 9.3 Unwrapping with deep learning model 9.4 Conclusions References 10. Noise-free phase imaging in Gabor DHM with deep learning 10.1 Introduction 10.2 A deep learning model for Gabor DHM 10.3 Experimental results 10.4 Discussion 10.5 Conclusions References   Part III. Intelligent DHM for Biomedical Applications 11. Introduction References 12. Red blood cells phase image segmentation 12.1 Introduction 12.2 Marker-controlled watershed algorithm 12.3 Segmentation based on marker-controlled watershed algorithm 12.4 Experimental results 12.5 Performance evaluation 12.6 Conclusions References 13. Red blood cells phase image segmentation with deep learning 13.1 Introduction 13.2 Fully convolutional neural networks 13.3 Red blood cells phase image segmentation via deep learning 13.4 Experimental results 13.5 Conclusions References 14. Automated phenotypic classification of red blood cells 14.1 Introduction  14.2 Feature extraction 14.3 Pattern recognition neural network 14.4 Experimental results and discussion 14.5 Conclusions References 15. Automated analysis of red blood cell storage lesions 15.1 Introduction 15.2 Quantitative analysis of red blood cell 3D morphological changes 15.3 Experimental results and discussion 15.4 Conclusions References 16. Automated red blood cells classification with deep learning 16.1 Introduction 16.2 Proposed deep learning model 16.3 Experimental results 16.4 Conclusions References 17. High-throughput label-free cell counting with deep neural networks 17.1 Introduction 17.2 Materials and methods 17.3 Experimental results 17.4 Conclusions References 18. Automated tracking of temporal displacements of red blood cells 18.1 Introduction 18.2 Mean-shift tracking algorithm 18.3 Kalman filter 18.4 Procedure for single RBC tracking 18.5 Experimental results 18.6 Conclusions References 19. Automated quantitative analysis of red blood cells dynamics 19.1 Introduction 19.2 Red blood cell parameters 19.3 Quantitative analysis of red blood cell fluctuations 19.4 Conclusions References 20. Quantitative analysis of red blood cells during temperature elevation 20.1 Introduction 20.2 Red blood cell sample preparations 20.3 Experimental results 20.4 Conclusions References 21. Automated measurement of cardiomyocytes dynamics with DHM 21.1 Introduction 21.2 Cell culture and imaging 21.3 Automated analysis of cardiomyocytes dynamics 21.4 Conclusions References 22. Automated analysis of cardiomyocytes with deep learning 22.1 Introduction 22.2 Region of interest identification with dynamic beating activity analysis 22.3 Deep neural network for cardiomyocytes image segmentation 22.4 Experimental results 22.5 Conclusions References 23. Automatic quantification of drug-treated cardiomyocytes with DHM 23.1 Introduction 23.2 Materials and methods 23.3 Experimental results and discussion 23.4 Conclusions References 24. Analysis of cardiomyocytes with holographic image-based tracking 24.1 Introduction 24.2 Materials and methods 24.3 Experimental results and discussion 24.4 Conclusions References 25. Conclusion and future work  

About the Author :
Inkyu Moon, PhD, is Professor in the Department of Robotics and Mechatronics Engineering at Daegu Gyeongbuk Institute of Science & Technology (DGIST), South Korea.