Artificial Intelligence in Digital Holographic Imaging Technical Basis and Biomedical ApplicationsAn 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.
Author(s): Inkyu Moon
Series: Wiley Series in Biomedical Engineering and Multi-Disciplinary Integrated Systems
Publisher: Wiley
Year: 2022
Language: English
Pages: 337
City: Hoboken
Cover
Title Page
Copyright Page
Contents
Preface
Part I Digital Holographic Imaging
Chapter 1 Introduction
References
Chapter 2 Coherent Optical Imaging
2.1 Monochromatic Fields and Irradiance
2.2 Analytic Expression for Fresnel Diffraction
2.3 Lens Transmittance Function
2.4 Geometrical Imaging Concepts
2.5 Coherent Imaging Theory
References
Chapter 3 Lateral and Depth Resolutions
3.1 Lateral Resolution
3.2 Depth (or Axial) Resolution
References
Chapter 4 Phase Unwrapping
4.1 Branch Cuts
4.2 Quality-guided, Path-following Algorithms
References
Chapter 5 Off-axis Digita Holographi Microscopy
5.1 Off-axis Digita Holographi Microscop Designs
5.2 Digita Hologra Reconstruction
References
Chapter 6 Gabo Digita Holographi Microscopy
6.1 Introduction
6.2 Methodology
References
Par II Dee Learning in Digital Holographic Microscopy (DHM)
Chapter 7 Introduction
References
Chapter 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
Chapter 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
Chapter 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 Digital Holographic Microscopy (DHM) for Biomedical Applications
Chapter 11 Introduction
References
Chapter 12 Red Blood Cell 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
Chapter 13 Red Blood Cell Phase-image Segmentation with Deep Learning
13.1 Introduction
13.2 Fully Convolutional Neural Networks
13.3 RBC Phase-image Segmentation via Deep Learning
13.4 Experimental Results
13.5 Conclusions
References
Chapter 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
Chapter 15 Automated Analysis of Red Blood Cell Storage Lesions
15.1 Introduction
15.2 Quantitative Analysis of RBC 3D Morphological Changes
15.3 Experimental Results and Discussion
15.4 Conclusions
References
Chapter 16 Automated Red Blood Cell Classification with Deep Learning
16.1 Introduction
16.2 Proposed Deep-learning Model
16.3 Experimental Results
16.4 Conclusions
References
Chapter 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
Chapter 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
Chapter 19 Automated Quantitative Analysis of Red Blood Cell Dynamics
19.1 Introduction
19.2 RBC Parameters
19.3 Quantitative Analysis of RBC Fluctuations
19.4 Conclusions
References
Chapter 20 Quantitative Analysis of Red Blood Cells during Temperature Elevation
20.1 Introduction
20.2 RBC Sample Preparations
20.3 Experimental Results
20.4 Conclusions
References
Chapter 21 Automated Measurement of Cardiomyocyte Dynamics with DHM
21.1 Introduction
21.2 Cell Culture and Imaging
21.3 Automated Analysis of Cardiomyocyte Dynamics
21.4 Conclusions
References
Chapter 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 Cardiomyocyte Image Segmentation
22.4 Experimental Results
22.5 Conclusions
References
Chapter 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
Chapter 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
Chapter 25 Conclusion and Future Work
Index
EULA
