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 recentl...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Moon, Inkyu (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : Wiley, [2023]
Colección:Wiley series in biomedical engineering and multi-disciplinary integrated systems.
Temas:
Three-dimensional imaging.
Artificial intelligence.
Three-dimensional imaging in medicine.
Holography > methods.
Microscopy > methods.
Artificial Intelligence.
Erythrocytes > pathology.
Myocytes, Cardiac > pathology.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.

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