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 Imaging

Chapter 1 Introduction

Chapter 2 Coherent Optical Imaging

Chapter 3 Lateral and Depth Resolutions

Chapter 4 Phase Unwrapping

Chapter 5 Off-axis Digita Holographi Microscopy

Chapter 6 Gabo Digita Holographi Microscopy

Part II Deep Learning in Digital Holographic Microscopy (DHM)

Chapter 7 Introduction

Chapter 8 No-search. Focus Prediction in DHM with Deep Learning

Chapter 9 Automated Phase Unwrapping in DHM with Deep Learning

Chapter 10 Noise-free Phase Imaging in Gabor DHM with Deep Learning

Part Ill Intelligent Digital Holographic Microscopy (DHM) for Biomedical Applications

Chapter 11 Introduction

Chapter 12 Red Blood Cell Phase-image Segmentation

Chapter 13 Red Blood Cell Phase-image Segmentation with Deep Learning

Chapter 14 Automated Phenotypic Classification of Red Blood Cells

Chapter 15 Automated Analysis of Red Blood Cell Storage Lesions

Chapter 16 Automated Red Blood Cell Classification with Deep Learning

Chapter 17 High-throughput Label-free Cell Counting with Deep Neural Networks

Chapter 18 Automated Tracking of Temporal Displacements of Red Blood Cells

Chapter 19 Automated Quantitative Analysis of Red Blood Cell Dynamics

Chapter 20 Quantitative Analysis of Red Blood Cells during Temperature Elevation

Chapter 21 Automated Measurement of Cardiomyocyte Dynamics with DHM

Chapter 22 Automated Analysis of Cardiomyocytes with Deep Learning

Chapter 23 Automatic Quantification of Drug-treated Cardiomyocytes with DHM

Chapter 24 Analysis of Cardiomyocytes with Holographic Image-based Tracking

Chapter 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.