Artificial Intelligence in Label-free Microscopy
-15%
portes grátis
Artificial Intelligence in Label-free Microscopy
Biological Cell Classification by Time Stretch
; ;
Springer International Publishing AG
04/2017
134
Dura
Inglês
9783319514475
15 a 20 dias
Descrição não disponível.
I Time stretch imaging1 Introduction2 Background2.1 Time Stretch Imaging2.2 Cell Classification Using Time Stretch Imaging2.3 Label-free Phenotypic Screening2.4 Warped Time Stretch for Data CompressionII Inspection and vision3 Nanometer-resolved imaging vibrometer3.1 Introduction3.2 Experimental demonstration3.3 Theoretical study of the vibrometer performance3.4 Experimental results3.5 Conclusion4 Three-dimensional ultrafast laser scanner4.1 Introduction4.2 Principle of hybrid dispersion laser scanner4.3 Applications of hybrid dispersion laser scannerIII Biomedical applications5 Label-free High-throughput Phenotypic Screening5.1 Introduction5.2 Experimental Setup5.3 Results and Discussion5.4 Conclusion6 Time Stretch Quantitative Phase Imaging6.1 Background6.2 Time Stretch Quantitative Phase Imaging6.2.1 Overview6.2.2 Imaging system6.2.3 System performance and resolvable points6.2.4 Microuidic channel design and fabrication6.2.5 Coherent Detection and Phase Extraction6.2.6 Cell Transmittance Extraction6.2.7 Image Reconstruction6.3 Image Processing Pipeline6.3.1 Feature Extraction6.3.2 Multivariate Features Enabled by Sensor Fusion6.3.3 System Calibration6.4 ConclusionIV Big data and artifitial intelligence7 Big data acquisition and processing in real-time7.1 Introduction7.2 Technical description of the acquisition system7.3 Big data acquisition results7.4 Conclusion8 Deep Learning and Classification8.1 Background8.2 Machine Learning8.3 Applications8.3.1 Blood Screening: Demonstration in Classification of OT-II and SW-480 Cells8.3.2 Biofuel: Demonstration in Algae Lipid Content Classification8.4 Further Discussions in Machine Learning8.4.1 Learning Curves8.4.2 Principal Component Analysis (PCA)8.4.3 Cross Validation8.4.4 Computation Time8.4.5 Data Cleaning8.5 ConclusionV Data compression9 Optical Data Compression in Time Stretch Imaging9.1 Background9.2 Warped Stretch Imaging9.3 Optical Image Compression9.4 Experimental Design and Results9.5 Conclusion10 Design of Warped Stretch Transform10.1 Overview10.2 Kernel Design10.2.1 Spectral Resolution10.2.2 Group Delay Profile Design10.2.3 Simulation Model10.2.4 Spectrograms10.3 Discussion10.4 Conclusion11 Concluding Remarks and Future WorkReferences
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I Time stretch imaging1 Introduction2 Background2.1 Time Stretch Imaging2.2 Cell Classification Using Time Stretch Imaging2.3 Label-free Phenotypic Screening2.4 Warped Time Stretch for Data CompressionII Inspection and vision3 Nanometer-resolved imaging vibrometer3.1 Introduction3.2 Experimental demonstration3.3 Theoretical study of the vibrometer performance3.4 Experimental results3.5 Conclusion4 Three-dimensional ultrafast laser scanner4.1 Introduction4.2 Principle of hybrid dispersion laser scanner4.3 Applications of hybrid dispersion laser scannerIII Biomedical applications5 Label-free High-throughput Phenotypic Screening5.1 Introduction5.2 Experimental Setup5.3 Results and Discussion5.4 Conclusion6 Time Stretch Quantitative Phase Imaging6.1 Background6.2 Time Stretch Quantitative Phase Imaging6.2.1 Overview6.2.2 Imaging system6.2.3 System performance and resolvable points6.2.4 Microuidic channel design and fabrication6.2.5 Coherent Detection and Phase Extraction6.2.6 Cell Transmittance Extraction6.2.7 Image Reconstruction6.3 Image Processing Pipeline6.3.1 Feature Extraction6.3.2 Multivariate Features Enabled by Sensor Fusion6.3.3 System Calibration6.4 ConclusionIV Big data and artifitial intelligence7 Big data acquisition and processing in real-time7.1 Introduction7.2 Technical description of the acquisition system7.3 Big data acquisition results7.4 Conclusion8 Deep Learning and Classification8.1 Background8.2 Machine Learning8.3 Applications8.3.1 Blood Screening: Demonstration in Classification of OT-II and SW-480 Cells8.3.2 Biofuel: Demonstration in Algae Lipid Content Classification8.4 Further Discussions in Machine Learning8.4.1 Learning Curves8.4.2 Principal Component Analysis (PCA)8.4.3 Cross Validation8.4.4 Computation Time8.4.5 Data Cleaning8.5 ConclusionV Data compression9 Optical Data Compression in Time Stretch Imaging9.1 Background9.2 Warped Stretch Imaging9.3 Optical Image Compression9.4 Experimental Design and Results9.5 Conclusion10 Design of Warped Stretch Transform10.1 Overview10.2 Kernel Design10.2.1 Spectral Resolution10.2.2 Group Delay Profile Design10.2.3 Simulation Model10.2.4 Spectrograms10.3 Discussion10.4 Conclusion11 Concluding Remarks and Future WorkReferences
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