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Artificial Intelligence in Digital Holographic Imaging

Technical Basis and Biomedical Applications

Moon, Inkyu

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1. Auflage Dezember 2023
336 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-0-470-64750-9
John Wiley & Sons

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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.

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

I. Moon, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu, South Korea