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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 宋孔彬(Kung-Bin Sung) | |
| dc.contributor.author | Yang-Hsien Lin | en |
| dc.contributor.author | 林仰賢 | zh_TW |
| dc.date.accessioned | 2021-06-16T02:45:42Z | - |
| dc.date.available | 2022-07-23 | |
| dc.date.copyright | 2020-08-21 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-12 | |
| dc.identifier.citation | 1. R. Barer, 'Interference microscopy and mass determination,' Nature 169(4296), 366-367 (1952). 2. M. T. Rinehart, H. S. Park, and A. Wax, 'Influence of defocus on quantitative analysis of microscopic objects and individual cells with digital holography,' Biomedical optics express 6(6), 2067-2075 (2015). [doi: 10.1364/BOE.6.002067] 3. M. Diez-Silva et al., 'Shape and biomechanical characteristics of human red blood cells in health and disease,' MRS bulletin 35(05), 382-388 (2010). [doi: 10.1557/mrs2010.571] 4. G. Dhaliwal, P. A. Cornett, and L. M. Tierney Jr, 'Hemolytic anemia,' Am Fam Physician 69(11), 2599-2606 (2004). 5. D. G. H. Silva et al., 'Oxidative stress in sickle cell disease: An overview of erythrocyte redox metabolism and current antioxidant therapeutic strategies,' Free Radical Biology and Medicine 65(1101-1109 (2013). [doi: 10.1016/j.freeradbiomed.2013.08.181] 6. M. Schrier, Stanley L, 'Thalassemia: pathophysiology of red cell changes,' Annual review of medicine 45(1), 211-218 (1994). [doi: 10.1146/annurev.med.45.1.211] 7. K. Gilev et al., 'Advanced consumable‐free morphological analysis of intact red blood cells by a compact scanning flow cytometer,' Cytometry Part A (2017). [doi: 10.1002/cyto.a.23141] 8. K. V. Gilev et al., 'Mature red blood cells: from optical model to inverse light-scattering problem,' Biomedical optics express 7(4), 1305-1310 (2016). [doi: 10.1364/BOE.7.001305] 9. F. Merola et al., 'Phase contrast tomography at lab on chip scale by digital holography,' Methods 136(108-115 (2018). [doi: 10.1016/j.ymeth.2018.01.003] 10. M. Mugnano et al., 'Label-Free Optical Marker for Red-Blood-Cell Phenotyping of Inherited Anemias,' Analytical chemistry 90(12), 7495-7501 (2018). [doi:10.1021/acs.analchem.8b01076] 11. F. Merola et al., 'Tomographic flow cytometry by digital holography,' Light: Science Applications 6(4), e16241 (2017). [doi:10.1038/lsa.2016.241] 12. D. Dannhauser et al., 'Optical signature of erythrocytes by light scattering in microfluidic flows,' Lab on a Chip 15(16), 3278-3285 (2015). [doi: 10.1039/C5LC00525F] 13. M. Kugeiko, and D. Smunev, 'Method for Determining Erythrocyte Surface Area by Polarization and Nephelometric Measurements,' Journal of Applied Spectroscopy 82(6), 985-992 (2016). [doi: 10.1007/s10812-016-0216-2] 14. J. W. Su et al., 'Digital holographic microtomography for high‐resolution refractive index mapping of live cells,' Journal of biophotonics 6(5), 416-424 (2013). [doi: 10.1002/jbio.201200022] 15. I. Moon et al., 'Automated statistical quantification of three-dimensional morphology and mean corpuscular hemoglobin of multiple red blood cells,' Optics express 20(9), 10295-10309 (2012). [doi: 10.1364/OE.20.010295] 16. F. Yi, I. Moon, and Y. H. Lee, 'Three-dimensional counting of morphologically normal human red blood cells via digital holographic microscopy,' Journal of biomedical optics 20(1), 016005-016005 (2015). [doi: 10.1117/1.JBO.20.1.016005] 17. F. Yi, I. Moon, and B. Javidi, 'Cell morphology-based classification of red blood cells using holographic imaging informatics,' Biomedical optics express 7(6), 2385-2399 (2016). [doi: 10.1364/BOE.7.002385] 18. B. J. Bain, 'Diagnosis from the blood smear,' New England Journal of Medicine 353(5), 498-507 (2005). [doi:10.1056/NEJMra043442] 19. J. Ford, 'Red blood cell morphology,' International journal of laboratory hematology 35(3), 351-357 (2013). [doi:10.1111/ijlh.12082] 20. A. Adewoyin, 'Peripheral blood film-a review,' Annals of Ibadan postgraduate medicine 12(2), 71-79 (2014). 21. M. Ugele et al., 'Label-free, high-throughput detection of P. falciparum infection in sphered erythrocytes with digital holographic microscopy,' Lab on a Chip 18(12), 1704-1712 (2018). [doi:10.1039/c8lc00350e] 22. N. Lue et al., 'Live cell refractometry using microfluidic devices,' Optics letters 31(18), 2759-2761 (2006). [doi:10.1364/OL.31.002759] 23. G. Kim et al., 'Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells,' Biosensors and Bioelectronics 123(69-76 (2019). [doi: 10.1016/j.bios.2018.09.068] 24. Y.-H. Lin et al., 'Morphometric analysis of erythrocytes from patients with thalassemia using tomographic diffractive microscopy,' Journal of biomedical optics 22(11), 116009 (2017). [doi: 10.1117/1.JBO.22.11.116009] 25. M. T. Rinehart et al., 'Hemoglobin consumption by P. falciparum in individual erythrocytes imaged via quantitative phase spectroscopy,' Scientific reports 6(24461 (2016). [doi:10.1038/srep24461] 26. H. S. Park et al., 'Automated detection of P. falciparum using machine learning algorithms with quantitative phase images of unstained cells,' PloS one 11(9), e0163045 (2016). [doi:10.1371/journal.pone.0163045] 27. A. Anand et al., 'Automatic identification of malaria-infected RBC with digital holographic microscopy using correlation algorithms,' IEEE Photonics Journal 4(5), 1456-1464 (2012). [doi:10.1109/JPHOT.2012.2210199] 28. B. Javidi et al., 'Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy,' Optics express 26(10), 13614-13627 (2018). [doi: 10.1364/OE.26.013614] 29. N. T. Shaked et al., 'Quantitative microscopy and nanoscopy of sickle red blood cells performed by wide field digital interferometry,' Journal of biomedical optics 16(3), 030506 (2011). [doi:10.1117/1.3556717] 30. J. Jung et al., 'Optical characterization of red blood cells from individuals with sickle cell trait and disease in Tanzania using quantitative phase imaging,' Scientific reports 6(31698 (2016). [doi:10.1038/srep31698] 31. F. Yi et al., 'Automated segmentation of multiple red blood cells with digital holographic microscopy,' Journal of Biomedical Optics 18(2), 026006 (2013). [doi:10.1117/1.JBO.18.2.026006.] 32. Y. Jo et al., 'Quantitative phase imaging and artificial intelligence: a review,' IEEE Journal of Selected Topics in Quantum Electronics 25(1), 1-14 (2018). [doi:10.1109/JSTQE.2018.2859234] 33. G. Litjens et al., 'A survey on deep learning in medical image analysis,' Medical image analysis 42(60-88 (2017). [doi:10.1016/j.media.2017.07.005] 34. G. Kim et al., 'Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning,' BioRxiv 596486 (2019). [doi:10.1101/596486] 35. Y. Jo et al., 'Holographic deep learning for rapid optical screening of anthrax spores,' Science advances 3(8), e1700606 (2017). [doi:10.1126/sciadv.1700606] 36. Y. Zhang et al., 'Computational cytometer based on magnetically modulated coherent imaging and deep learning,' Light: Science Applications 8(1), 1-15 (2019). [doi:10.1038/s41377-019-0203-5] 37. M. Xu et al., 'A deep convolutional neural network for classification of red blood cells in sickle cell anemia,' PLoS computational biology 13(10), (2017). [doi:10.1371/journal.pcbi.1005746] 38. K. de Haan et al., 'Automated screening of sickle cells using a smartphone-based microscope and deep learning,' arXiv preprint arXiv:1912.05155 (2019). [doi: 10.1038/s41746-020-0282-y] 39. W. D. Pan, Y. Dong, and D. Wu, 'Classification of malaria-infected cells using deep convolutional neural networks,' Machine Learning: Advanced Techniques and Emerging Applications 159((2018). [doi:10.5772/intechopen.72426] 40. L. Alzubaidi et al., 'Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis,' Electronics 9(3), 427 (2020). [doi:10.3390/electronics9030427] 41. K. He et al., 'Mask r-cnn,' Computer Vision (ICCV), 2017 IEEE International Conference on 2980-2988 (2017). 42. M. S. Durkee et al., 'Improved instance segmentation of immune cells in human lupus nephritis biopsies with Mask R-CNN,' Medical Imaging 2020: Digital Pathology 1132019 (2020). 43. H. Jung, B. Lodhi, and J. Kang, 'An automatic nuclei segmentation method based on deep convolutional neural networks for histopathology images,' BMC Biomedical Engineering 1(1), 24 (2019). [doi:10.1186/s42490-019-0026-8] 44. N. Moshkov et al., 'Test-time augmentation for deep learning-based cell segmentation on microscopy images,' Scientific reports 10(1), 1-7 (2020). [doi:10.1038/s41598-020-61808-3] 45. X. Xie et al., 'Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN,' International MICCAI Brainlesion Workshop 428-436 (2018). 46. N. Ramkumar, and B. Baum, 'Coupling changes in cell shape to chromosome segregation,' Nature Reviews Molecular Cell Biology 17(8), 511-521 (2016). [doi: 10.1038/nrm.2016.75] 47. C. L. Dix et al., 'The role of mitotic cell-substrate adhesion re-modeling in animal cell division,' Developmental cell 45(1), 132-145. e133 (2018). [doi: 10.1016/j.devcel.2018.03.009] 48. K. Crasta et al., 'DNA breaks and chromosome pulverization from errors in mitosis,' Nature 482(7383), 53-58 (2012). [doi: 10.1038/nature10802] 49. S. Santaguida, and A. Amon, 'Short-and long-term effects of chromosome mis-segregation and aneuploidy,' Nature reviews Molecular cell biology 16(8), 473-485 (2015). [doi: 10.1038/nrm4025] 50. Z. Liang et al., 'Chromosomes progress to metaphase in multiple discrete steps via global compaction/expansion cycles,' Cell 161(5), 1124-1137 (2015). [doi: 10.1016/j.cell.2015.04.030] 51. D. J. Stephens, and V. J. Allan, 'Light microscopy techniques for live cell imaging,' science 300(5616), 82-86 (2003). [doi: 10.1126/science.1082160] 52. M. M. Frigault et al., 'Live-cell microscopy–tips and tools,' Journal of cell science 122(6), 753-767 (2009). [doi: 10.1242/jcs.033837] 53. S. Huh et al., 'Automated mitosis detection of stem cell populations in phase-contrast microscopy images,' IEEE transactions on medical imaging 30(3), 586-596 (2010). [doi: 10.1109/TMI.2010.2089384] 54. A. Liu, K. Li, and T. Hao, 'A hierarchical framework for mitosis detection in time-lapse phase contrast microscopy image sequences of stem cell populations,' Medical Imaging 355 (2011). [doi: 10.5772/34684] 55. A. Liu et al., 'Nonnegative mixed-norm convex optimization for mitotic cell detection in phase contrast microscopy,' Computational and mathematical methods in medicine 2013((2013). [doi: 10.1155/2013/176272] 56. K. Thirusittampalam et al., 'A novel framework for cellular tracking and mitosis detection in dense phase contrast microscopy images,' IEEE journal of biomedical and health informatics 17(3), 642-653 (2013). [doi: 10.1109/TITB.2012.2228663] 57. O. Debeir et al., 'Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes,' IEEE transactions on medical imaging 24(6), 697-711 (2005). [doi: 10.1109/TMI.2005.846851] 58. S. Huh, and M. Chen, 'Detection of mitosis within a stem cell population of high cell confluence in phase-contrast microscopy images,' CVPR 2011 1033-1040 (2011). 59. P. Girshovitz, and N. T. Shaked, 'Generalized cell morphological parameters based on interferometric phase microscopy and their application to cell life cycle characterization,' Biomedical optics express 3(8), 1757-1773 (2012). [doi: 10.1364/BOE.3.001757] 60. M. Mir et al., 'Optical measurement of cycle-dependent cell growth,' Proceedings of the National Academy of Sciences 108(32), 13124-13129 (2011). [doi: 10.1073/pnas.1100506108] 61. W. Choi et al., 'Tomographic phase microscopy,' Nature methods 4(9), 717-719 (2007). [doi: 10.1038/nmeth1078] 62. Y. Sung et al., 'Stain-free quantification of chromosomes in live cells using regularized tomographic phase microscopy,' PloS one 7(11), (2012). [doi: 10.1371/journal.pone.0049502] 63. F. Yang et al., 'Cell segmentation, tracking, and mitosis detection using temporal context,' International Conference on Medical Image Computing and Computer-Assisted Intervention 302-309 (2005). 64. W. Nie, H. Cheng, and Y. Su, 'Modeling temporal information of mitotic for mitotic event detection,' IEEE Transactions on Big Data 3(4), 458-469 (2017). [doi: 10.1109/TBDATA.2017.2723395] 65. C.-H. Huang, and H.-K. Lee, 'Automated mitosis detection based on exclusive independent component analysis,' Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012) 1856-1859 (2012). 66. Y. Su et al., 'Cell type-independent mitosis event detection via hidden-state conditional neural fields,' 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) 222-225 (2014). 67. W.-C. Hsu et al., 'Tomographic diffractive microscopy of living cells based on a common-path configuration,' Optics letters 39(7), 2210-2213 (2014). [doi: 10.1364/OL.39.002210] 68. G. Popescu et al., 'Diffraction phase microscopy for quantifying cell structure and dynamics,' Optics letters 31(6), 775-777 (2006). [doi: 10.1364/OL.31.000775] 69. P. Girshovitz, and N. T. Shaked, 'Fast phase processing in off-axis holography using multiplexing with complex encoding and live-cell fluctuation map calculation in real-time,' Optics express 23(7), 8773-8787 (2015). [doi: 10.1364/OE.23.008773] 70. M. A. Schofield, and Y. Zhu, 'Fast phase unwrapping algorithm for interferometric applications,' Optics letters 28(14), 1194-1196 (2003). [doi: 10.1364/OL.28.001194] 71. H. Pham et al., 'Off-axis quantitative phase imaging processing using CUDA: toward real-time applications,' Biomedical optics express 2(7), 1781-1793 (2011). [doi: 10.1364/BOE.2.001781] 72. O. Backoach et al., 'Fast phase processing in off-axis holography by CUDA including parallel phase unwrapping,' Optics express 24(4), 3177-3188 (2016). [doi: 10.1364/OE.24.003177] 73. G. Dardikman et al., 'Video-rate processing in tomographic phase microscopy of biological cells using CUDA,' Optics express 24(11), 11839-11854 (2016). [doi: 10.1364/OE.24.011839] 74. S. R. Deans, The Radon transform and some of its applications, Courier Corporation (2007). 75. E. Wolf, 'Three-dimensional structure determination of semi-transparent objects from holographic data,' Optics Communications 1(4), 153-156 (1969). [doi: 10.1016/0030-4018(69)90052-2] 76. A. Devaney, 'Inverse-scattering theory within the Rytov approximation,' Optics letters 6(8), 374-376 (1981). [doi: 10.1364/OL.6.000374] 77. Y. Sung et al., 'Optical diffraction tomography for high resolution live cell imaging,' Optics express 17(1), 266-277 (2009). [doi: 10.1364/OE.17.000266] 78. N. Otsu, 'A threshold selection method from gray-level histograms,' Automatica 11(285-296), 23-27 (1975). [doi: 10.1109/TSMC.1979.4310076] 79. R. Adams, and L. Bischof, 'Seeded region growing,' IEEE Transactions on pattern analysis and machine intelligence 16(6), 641-647 (1994). [doi: 10.1109/34.295913] 80. J. Hellmers, E. Eremina, and T. Wriedt, 'Simulation of light scattering by biconcave Cassini ovals using the nullfield method with discrete sources,' Journal of Optics A: Pure and Applied Optics 8(1), 1 (2005). 81. W. Krauze et al., 'Generalized total variation iterative constraint strategy in limited angle optical diffraction tomography,' Optics Express 24(5), 4924-4936 (2016). [doi: 10.1364/OE.24.004924] 82. D. H. Chui, S. Fucharoen, and V. Chan, 'Hemoglobin H disease: not necessarily a benign disorder,' Blood 101(3), 791-800 (2003). [doi: 10.1182/blood-2002-07-1975] 83. H. Byun et al., 'Optical measurement of biomechanical properties of individual erythrocytes from a sickle cell patient,' Acta biomaterialia 8(11), 4130-4138 (2012). [doi: 10.1016/j.actbio.2012.07.011] 84. Y. Kim et al., 'Profiling individual human red blood cells using common-path diffraction optical tomography,' Scientific reports 4(6659 (2014). [doi: 10.1038/srep06659] 85. B. Rappaz et al., 'Comparative study of human erythrocytes by digital holographic microscopy, confocal microscopy, and impedance volume analyzer,' Cytometry Part A 73(10), 895-903 (2008). [doi: 10.1002/cyto.a.20605] 86. I. Saytashev et al., 'Multiphoton excited hemoglobin fluorescence and third harmonic generation for non-invasive microscopy of stored blood,' Biomedical Optics Express 7(9), 3449-3460 (2016). [doi: 10.1364/BOE.7.003449] 87. B. Blasi et al., 'Red blood cell storage and cell morphology,' Transfusion medicine 22(2), 90-96 (2012). [doi: 10.1111/j.1365-3148.2012.01139.x] 88. Y. Park et al., 'Light scattering of human red blood cells during metabolic remodeling of the membrane,' Journal of biomedical optics 16(1), 011013-011013-011017 (2011). [doi: 10.1117/1.3524509] 89. S. Lee et al., 'Refractive index tomograms and dynamic membrane fluctuations of red blood cells from patients with diabetes mellitus,' Scientific Reports 7((2017). [doi: 10.1038/s41598-017-01036-4] 90. K. Kim et al., 'High-resolution three-dimensional imaging of red blood cells parasitized by Plasmodium falciparum and in situ hemozoin crystals using optical diffraction tomography,' Journal of biomedical optics 19(1), 011005 (2013). [doi:10.1117/1.JBO.19.1.011005] 91. P. Müller, M. Schürmann, and J. Guck, 'The Theory of Diffraction Tomography,' arXiv preprint arXiv:1507.00466 (2015). 92. R. Drezek, A. Dunn, and R. Richards-Kortum, 'A pulsed finite-difference time-domain (FDTD) method for calculating light scattering from biological cells over broad wavelength ranges,' Optics Express 6(7), 147-157 (2000). [doi: 10.1364/OE.6.000147] 93. L. Bi, and P. Yang, 'Modeling of light scattering by biconcave and deformed red blood cells with the invariant imbedding T-matrix method,' Journal of biomedical optics 18(5), 055001-055001 (2013). [doi: 10.1117/1.JBO.18.5.055001] 94. G.-S. Chao, and K.-B. Sung, 'Investigating the spectral characteristics of backscattering from heterogeneous spherical nuclei using broadband finite-difference time-domain simulations,' Journal of biomedical optics 15(1), 015007-015007-015006 (2010). [doi: 10.1117/1.3324838] 95. A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'Imagenet classification with deep convolutional neural networks,' Advances in neural information processing systems 1097-1105 (2012). 96. K. Simonyan, and A. Zisserman, 'Very deep convolutional networks for large-scale image recognition,' arXiv preprint arXiv:1409.1556 (2014). 97. C. Shorten, and T. M. Khoshgoftaar, 'A survey on image data augmentation for deep learning,' Journal of Big Data 6(1), 60 (2019). [doi:10.1186/s40537-019-0197-0] 98. M. Hall-Beyer, 'GLCM texture: A tutorial,' National Council on Geographic Information and Analysis Remote Sensing Core Curriculum 3((2000). [doi:10.11575/PRISM/33280] 99. K. Kono et al., 'Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co‐occurrence matrix,' Journal of clinical laboratory analysis 32(1), e22175 (2018). [doi:10.1002/jcla.22175] 100. T. Chen, and C. Guestrin, 'Xgboost: A scalable tree boosting system,' Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 785-794 (2016). 101. V. Janko et al., 'A New Frontier for Activity Recognition: The Sussex-Huawei Locomotion Challenge,' Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers 1511-1520 (2018). 102. S. M. Lundberg, and S.-I. Lee, 'A unified approach to interpreting model predictions,' Advances in Neural Information Processing Systems 4765-4774 (2017). 103. H. Hotelling, 'Relations between two sets of variates,' in Breakthroughs in statistics, pp. 162-190, Springer (1992). 104. D. Arifler, 'Sensitivity of spatially resolved reflectance signals to coincident variations in tissue optical properties,' Applied optics 49(22), 4310-4320 (2010). [doi:10.1364/AO.49.004310] 105. Y. Park et al., 'Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum,' Proceedings of the National Academy of Sciences 105(37), 13730-13735 (2008). [doi:10.1073/pnas.0806100105] 106. H. Park et al., 'Measuring cell surface area and deformability of individual human red blood cells over blood storage using quantitative phase imaging,' Scientific reports 6(34257 (2016). [doi:10.1038/srep34257] 107. S. Y. Lee et al., 'The effects of ethanol on the morphological and biochemical properties of individual human red blood cells,' PloS one 10(12), (2015). [doi:10.1371/journal.pone.0145327] 108. H. Park et al., 'Three-dimensional refractive index tomograms and deformability of individual human red blood cells from cord blood of newborn infants and maternal blood,' Journal of biomedical optics 20(11), 111208-111208 (2015). [doi: 10.1117/1.JBO.20.11.111208] 109. P. Memmolo et al., '3D morphometry of red blood cells by digital holography,' Cytometry part A 85(12), 1030-1036 (2014). [doi:10.1002/cyto.a.22570] 110. D. Meuten, F. Moore, and J. George, 'Mitotic count and the field of view area: time to standardize,' SAGE Publications Sage CA: Los Angeles, CA (2016). 111. C. A. Bertram et al., 'Computerized calculation of mitotic count distribution in canine cutaneous mast cell tumor sections: Mitotic count is area dependent,' Veterinary pathology 57(2), 214-226 (2020). [doi: 10.1177/0300985819890686] 112. D. Cai et al., 'Efficient mitosis detection in breast cancer histology images by RCNN,' 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) 919-922 (2019). 113. W.-Z. Nie et al., '3D convolutional networks-based mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations,' Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 55-62 (2016). 114. Y.-T. Su et al., 'Spatiotemporal joint mitosis detection using CNN-LSTM network in time-lapse phase contrast microscopy images,' IEEE Access 5(18033-18041 (2017). [doi: 10.1109/ACCESS.2017.2745544] 115. Y. Mao, and Z. Yin, 'A hierarchical convolutional neural network for mitosis detection in phase-contrast microscopy images,' International Conference on Medical Image Computing and Computer-Assisted Intervention 685-692 (2016). 116. H. Chen et al., 'Mitosis detection in breast cancer histology images via deep cascaded networks,' Thirtieth AAAI Conference on Artificial Intelligence (2016). 117. S. Albarqouni et al., 'Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images,' IEEE transactions on medical imaging 35(5), 1313-1321 (2016). [doi: 10.1109/TMI.2016.2528120] 118. C. Li et al., 'DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks,' Medical image analysis 45(121-133 (2018). [doi:10.1016/j.media.2017.12.002] | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54228 | - |
| dc.description.abstract | 無標記定量相位顯微術以光束穿透細胞後，獲取細胞內部組成差異所造成的二維相位差分布，該量測到的分布是由穿透光沿光軸通過樣本後的折射率線積分。依據繞射斷層掃描術原理，可藉由獲取不同角度下的二維相位影像進行重建而得到樣本的實際厚度與三維折射率分布影像。為了達到高通量掃描的之目的，本論文開發二維相位影像回復與三維折射率分布影像重建之平行運算來提升成像效率。 由於紅血球型態變異資訊可作為辨別血液相關疾病的診斷依據，本論文採用共光路式斷層繞射顯微術獲取紅血球的三維折射率分布影像，藉由定量健康紅血球與海洋性貧血紅血球的平均折射率、相位分布、光學體積與三維形態特徵來區分健康受試者與具有海洋性貧血之患者。其結果顯示由海洋性貧血紅血球較正常紅血球具有較小的光學體積與表面積與體積比值、球型指數和表面積四個特徵所建立的判別模型可獲得優異的分類結果，證實斷層繞射顯微術在區分健康與海洋性貧血紅血球上相當具有潛力。 雖然透過斷層繞射顯微術可以準確地區分出輕度海洋性貧血患者與正常受檢者，但其光學系統架構的複雜度和資料擷取與處理效率導致該系統難以直接應用於臨床上。為了解決此問題，本論文亦採用數位全像顯微術獲取紅血球的二維相位影像並使用遮罩區域卷積神經網路技術建立點對點分類模型獲得比傳統影像處理自動偵測更高的效率，並由該模型分割出的紅血球區域進行定量相位影像相關特徵計算，分析其與三維折射率影像特徵之相關性。 為了充分利用斷層繞射顯微術可獲得定量影像之優勢，本論文採用時序性掃描獲取一系列的視網膜色素細胞行有絲分裂的三維折射率分布影像。由於有絲分裂是一動態過程，因此藉由量化視網膜色素細胞的相位差統計、幾何型態變化與運動向量場來建立偵測與預測有絲分裂發生之模型。本實驗所建立的有絲分裂偵測與預測模型在分類準確度上皆高達100%的辨別結果，因此藉由模型的判辨能力將有助於分析單一細胞在微環境改變下的反應。 | zh_TW |
| dc.description.abstract | Label-free quantitative phase imaging (QPI) is capable of mapping in two dimensions the phase shift caused by cellular constituents when a source light been transmits though a transparent cell. The measured phase shift represents the line integral of the refractive index (RI) contrast between the specimen and its environment along the light path which is parallel to the optical axis and corresponds to physical thickness of the specimen. The physical thickness and three-dimensional (3-D) RI maps of specimen can be reconstructed from multiple two-dimensional (2-D) phase images acquired at various illumination directions using diffraction tomography techniques. To achieve high-throughput screening, a self-programmed software was developed to enhanced the efficiency by parallelizing the computation of 2-D phase retrieval and 3-D RI tomogram reconstruction. Since altered red blood cell (RBC) morphology is an important feature in distinguishing a variety of blood-related diseases, tomographic diffractive microscopy (TDM) was used to acquire 3-D RI tomograms of RBCs. The mean of refractive index, distribution of phase shift, optical volume and 3-D morphological features of healthy RBCs and thalassemic RBCs were measured to distinguish healthy subjects and patients with thalassemia. A multi-indices prediction model achieved perfect accuracy of diagnosing thalassemia using four features including the optical volume, surface-area-to-volume ratio, sphericity index and surface area. The results demonstrate abilities of TDM to provide quantitative, hematologic measurements and assess morphological features of erythrocytes to distinguish healthy and thalassemic erythrocytes. Although excellent accuracy of distinguishing thalassemia-minor patients from healthy subjects has been demonstrated, the complexity of the instrument, and inefficiencies in both data acquisition and data processing prevent the technique to become a clinical tool. To address the issues, digital holographic microscopy (DHM) was used to acquire 2-D QPI data of RBCs, and the Mask region-based convolutional neural network (R-CNN) technique was implemented to perform end-to-end classification with higher detection efficiency than conventional image processing-based methods. In addition, features extracted from quantitative phase images of RBCs segmented automatically by the Mask R-CNN model to characterize QPI features of thalassemia, and analyzed correlations between the 2-D QPI features and those extracted from 3-D RI tomograms of the same RBCs. To fully exploit TDM, a sequence of time-lapse RI tomograms of in vitro retinal pigment epithelial (RPE) cell was acquired to record the evolution of mitosis. Since mitosis is a dynamic process, the optical statistics, cell morphology and motion vector fields of RPE cell were characterized to build models for detecting and predicting mitotic events. Both the mitotic classifier and predictor have achieved perfect accuracy (100%) that can aid the single cell analysis by investigating cellular response to changes in the microenvironment. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T02:45:42Z (GMT). No. of bitstreams: 1 U0001-0308202018362600.pdf: 5506434 bytes, checksum: e6c677c6e2673d094acfcaec6022b175 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES xiii LIST OF ABBREVIATIONS xiv Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Imaging of living single cells 1 1.1.2 Morphometric analysis of erythrocytes 3 1.1.3 Automatic detection and characterization of thalassemic red blood cells 5 1.1.4 Mitosis identification in time-lapse imaging 8 1.2 Motivation 11 1.3 Research objective and dissertation overview 14 Chapter 2 Common-path Tomographic Diffractive Microscopy 15 2.1 Optical setup 19 2.2 System design requirements 22 2.3 Acceleration of phase reconstruction 23 2.4 3-D RI tomogram reconstruction 28 2.5 Performance evaluation of reconstruction on phase maps and RI tomograms 32 Chapter 3 Morphometric Analysis of Erythrocytes from 3-D Refractive Index Distributions 33 3.1 Materials and Methods 33 3.1.1 RBC sample preparation and data acquisition 33 3.1.2 RBC segmentation from 3-D RI tomograms 34 3.1.3 Calculation of optical indices and geometric indices 36 3.1.4 Evaluation of the 3-D ARG segmentation method 38 3.2 Results 39 3.2.1 Validation of three-dimensional adaptive region growing 39 3.2.2 Evaluation of the accuracy of the 3-D ARG method 40 3.2.3 Performances of various indices to distinguish thalassemia 45 3.3 Discussion 49 Chapter 4 Automatic Detection and Characterization of Quantitative Phase Images of Thalassemic Red Blood Cells using Mask Regional Convolution Neural Network 55 4.1 Data preparation for modeling 55 4.2 Automatic detection and characterization of RBCs 56 4.2.1 Development of the Mask R-CNN model 56 4.2.2 Development of the benchmark classifier using XGBoost 57 4.2.3 Results of RBCs detection and delineation 58 4.3 Characterization of thalassemic RBC quantitative phase images 60 4.4 Correlations between features extracted from 2-D QPI data and 3-D RI maps of RBCs 62 4.4.1 Procedure of the canonical correlation analysis (CCA) 62 4.4.2 Comparison between features extracted from 2-D QPI data and 3-D RI maps of RBCs 64 4.5 Discussion 65 Chapter 5 Detection and Prediction of Mitosis in Time-lapse Tomographic Diffractive Microscopy 68 5.1 RPE cell culture and long-term imaging 68 5.2 Estimation of dense volumetric vector fields 70 5.3 RPE characterization and mitotic model building 71 5.3.1 Morphological changes during mitotic cell cycle 71 5.3.2 Identification of mitotic event 74 5.3.3 Prediction of mitosis events 75 5.4 Discussion 76 Chapter 6 Conclusion and Future Work 79 References 82 | |
| dc.language.iso | en | |
| dc.title | 使用共光路斷層繞射顯微術之單細胞幾何與光學體積研究 | zh_TW |
| dc.title | Analyses of Individual Cell Morphology and Optical Volume with Common-path Tomographic Diffractive Microscopy | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 傅楸善(Chiou-Shann Fuh),劉冠良(Kuan-Liang Liu),吳尚儒(Shang-Ju Wu),郭柏齡(Po-Ling Kuo) | |
| dc.subject.keyword | 斷層繞射顯微術,紅血球,海洋性貧血,視網膜色素細胞,有絲分裂, | zh_TW |
| dc.subject.keyword | tomographic diffractive microscopy,red blood cells,thalassemia,retinal pigment epithelium cell,mitosis, | en |
| dc.relation.page | 90 | |
| dc.identifier.doi | 10.6342/NTU202002308 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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