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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 (ANN), convolutional neural networks (CNN), and generative adversarial network (GAN). 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
Digital Holographic Imaging
Coherent Optical Imaging
Lateral and Depth Resolutions
Phase Unwrapping
Off-axis Digital Holographic Microscopy
Gabor Digital Holographic Microscopy
Deep Learning in Digital Holographic Microscopy (DHM)
No-search Focus Prediction in DHM with Deep Learning
Automated Phase Unwrapping in DHM with Deep Learning
Noise-free Phase Imaging in Gabor DHM with Deep Learning
Intelligent Digital Holographic Microscopy (DHM) for Biomedical Applications
Red Blood Cell Phase-image Segmentation
Red Blood Cell Phase-image Segmentation with Deep Learning
Automated Phenotypic Classification of Red Blood Cells
Automated Analysis of Red Blood Cell Storage Lesions
Automated Red Blood Cell Classification with Deep Learning
High-throughput Label-free Cell Counting with Deep Neural Networks
Automated Tracking of Temporal Displacements of Red Blood Cells
Automated Quantitative Analysis of Red Blood Cell Dynamics
Quantitative Analysis of Red Blood Cells during Temperature Elevation
Automated Measurement of Cardiomyocyte Dynamics with DHM
Automated Analysis of Cardiomyocytes with Deep Learning
Automatic Quantification of Drug-treated Cardiomyocytes with DHM
Analysis of Cardiomyocytes with Holographic Image-based Tracking
Conclusion and Future Work