研究生: |
黃崇軒 Huang, Chung-Hsuan |
---|---|
論文名稱: |
免標記多模態全像斷層造影技術與應用之研究 Studies on Label-free Multimodal Holographic Tomography and Applications |
指導教授: |
鄭超仁
Cheng, Chau-Jern |
口試委員: |
林晃巖
Lin, Hoang Yan 陳建宇 Chen, Chien-Yu 杜翰艷 Tu, Han-Yen 陳皇銘 Chen, Huang-Ming 李翔傑 Lee, Hsiang-Chieh 楊承山 Yang, Chan-Shan |
口試日期: | 2024/07/18 |
學位類別: |
博士 Doctor |
系所名稱: |
光電工程研究所 Graduate Institute of Electro-Optical Engineering |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 93 |
中文關鍵詞: | 數位全像術 、全像斷層造影 、電腦全像術 、深度學習 、像差校正 、細胞死亡 、生物物理參數 、多模態系統 、拉曼光譜 |
英文關鍵詞: | digital holography, holographic tomography, computer-generated hologram, deep learning, aberration correction, cell death, biophysical parameters, multimodal system, Raman spectroscopy |
研究方法: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202401287 |
論文種類: | 學術論文 |
相關次數: | 點閱:269 下載:0 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
[1] J. Jonkman, C. M. Brown, G. D. Wright, K. I. Anderson, and A. J. North, “Tutorial: guidance for quantitative confocal microscopy,” Nat. Protoc. 15, 1585–1611 (2020).
[2] T. Shibata, T. Narita, Y. Suto, H. Yasmin, and T. Kabashima, “A Facile Fluorometric Assay of Orotate Phosphoribosyltransferase Activity Using a Selective Fluorogenic Reaction for Orotic Acid,” Sensors 23, 2507 (2023).
[3] S. M. Lewis, M. L. Asselin-Labat, Q. Nguyen, J. Berthelet, X. Tan, V. C. Wimmer, D. Merino, K. L. Rogers, and S. H. Naik, “Spatial omics and multiplexed imaging to explore cancer biology,” Nat. Methods 18, 997-1012 (2021).
[4] X. Wu, H. Liu, J. Liu, K. N. Haley, J. A. Treadway, J P. Larson, N. Ge, F. Peale, M. P. Bruchez, “Immunofluorescent labeling of cancer marker Her2 and other cellular targets with semiconductor quantum dots,” Nat. Biotechnol. 21, 41-46 (2003).
[5] S. Sarkar, P. Le, J. Geng, Y. Liu, Z. Han, M. U. Zahid, D. Nall, Y. Youn, P. R. Selvin, and A. M. Smith, “Short-Wave Infrared Quantum Dots with Compact Sizes as Molecular Probes for Fluorescence Microscopy,” J. Am. Chem. Soc. 142, 3449–3462 (2020).
[6] I. Y. Eremchev, N. A. Lozing, A. A. Baev, A. O. Tarasevich, M. G. Gladush, A. A. Rozhentsov, and A. V. Naumov, “Luminescence Microscopy of Single Quantum Dot Pairs with Nanometer Spatial Resolution,” JETP Lett. 108, 30-37 (2018).
[7] T. J. Kim, S. Türkcan, and G. Pratx, “Modular low-light microscope for imaging cellular bioluminescence and radioluminescence,” Nat. Protoc. 12, 1055-1076 (2017).
[8] A. J. Syed and J. C. Anderson, “Applications of bioluminescence in biotechnology and beyond,” Chem. Soc. Rev. 50, 5668-5705 (2021).
[9] S. Iwano, M. Sugiyama, H. Hama, A. Watakabe, N. Hasegawa, T. Kuchimaru, K. Z. Tanaka, M. Takahashi, Y. Ishida, J. Hata, S. Shimozono, K. Namiki, T. Fukano, M. Kiyama, H. Okano, S. Kizaka-Kondoh, T. J. McHugh, T. Yamamori, H. Hioki, S. Maki, and A. Miyawaki, “Single-cell bioluminescence imaging of deep tissue in freely moving animals,” Science 359, 935-939 (2018).
[10] Z. Liu, L. Tian, S. Liu, and L. Waller, “Real-time brightfield, darkfield, and phase contrast imaging in a light-emitting diode array microscope,” J. Bio. Opt. 19(10), 106002 (2014).
[11] R. Gallardo-Caballero, C. J. García-Orellana, A. García-Manso, H. M. González-Velasco, R. Tormo-Molina, and M. Macías-Macías, “Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques,” Sensors 19, 3583 (2019).
[12] S. Baar, M. Kuragano, K. Tokuraku, and S. Watanabe, “Towards a comprehensive approach for characterizing cell activity in bright-field microscopic images,” Sci. Rep. 12, 16884 (2022).
[13] J. Liu, Q. Yang, S. Chen, Z. Xiao, S. Wen, and H. Luo, “Intrinsic Optical Spatial Differentiation Enabled Quantum Dark-Field Microscopy,” Phys. Rev. Lett. 128, 193601 (2022).
[14] P. F. Gao, G. Lei, and C. Z. Huang, “Dark-Field Microscopy: Recent Advances in Accurate Analysis and Emerging Applications,” Anal. Chem. 93, 4707–4726 (2021).
[15] I. Ishmukhametov, L. Nigamatzyanova, G.r Fakhrullina, and R. Fakhrullin, “Label-free identification of microplastics in human cells: dark-field microscopy and deep learning study,” Anal. Bioanal. Chem. 414, 1297–1312 (2022).
[16] T. Vicar, J. Balvan, J. Jaros, F. Jug, R. Kolar, M. Masarik, and J. Gumulec, “Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison,” BMC Bioinformatics 20, 360 (2019).
[17] A. A. Liu, Y. Lu, M. Chen, and Y. T. Su, “Mitosis Detection in Phase Contrast Microscopy Image Sequences of Stem Cell Populations: A Critical Review,” IEEE Trans. Big Data 3, 443-457 (2017).
[18] K. Toda, M. Tamamitsu, Y. Nagashima, R. Horisaki, and T. Ideguchi, “Molecular contrast on phase-contrast microscope,” Sci. Rep. 9, 9957 (2019).
[19] M. Wagner and H. Horn, “Optical coherence tomography in biofilm research: A comprehensive review,” Biotechnol. Bioeng. 114, 1386-1402 (2017).
[20] N. Eladawi, M. Elmogy, M. Ghazal, O. Helmy, A. Aboelfetouh, A. Riad, S. Schaal, and A. El-Baz, “Classification of retinal diseases based on OCT Images,” Frontiers Biosci. 23, 64-247 (2018).
[21] C. Y. Ng, T. A. Wang, H. C. Lee, B. H. Huang, and M. T. Tsai, “In Vivo Identification of Skin Photodamage Induced by Fractional CO2 and Picosecond Nd:YAG Lasers with Optical Coherence Tomography,” Diagnostics 12, 822 (2022).
[22] Y. L. Lee, Y. C. Lin, H. Y. Tu, and C. J. Cheng, “Phase measurement accuracy in digital holographic microscopy using a wavelength-stabilized laser diode,” J. Opt. 15, 025403 (2013).
[23] P. Marquet, C. Depeursinge, and P. J. Magistretti, “Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders,” Neurophotonics 1, 020901 (2014).
[24] Z. El-Schich, A. L. Mölder, and A. G. Wingren, “Quantitative Phase Imaging for Label-Free Analysis of Cancer Cells—Focus on Digital Holographic Microscopy,” Appl. Sci. 8, 1027 (2018).
[25] F. Charriere, N. Pavillon, T. Colomb, C. Depeursinge, T. J. Heger, E. A. D. Mitchell, P. Marquet, and B. Rappaz, “Living specimen tomography by digital holographic microscopy: morphometry of testate amoeba,” Opt. Express 14, 7005-7013 (2006).
[26] S. Y. Lee, H. J. Park, K. Kim, Y. H. Sohn, S. Jang, Y. K. Park, “Refractive index tomograms and dynamic membrane fluctuations of red blood cells from patients with diabetes mellitus,” Sci. Rep. 7, 1039 (2017).
[27] V. Balasubramani, M. Kujawińska, C. Allier, V. Anand, C. J. Cheng, C. Depeursinge, N. Hai, S. Juodkazis, J. Kalkman, A. Kuś, M. Lee, P. J. Magistretti, P. Marquet, S. H. Ng, J. Rosen, Y. K. Park, and M. Ziemczonok, “Roadmap on Digital Holography-Based Quantitative Phase Imaging,” J. Imaging 7, 252 (2021).
[28] R. Smith, K. L. Wright, and L. Ashton, “Raman spectroscopy: an evolving technique for live cell studies,” Analyst 141, 3590-3600 (2016).
[29] H. J Butler, L. Ashton, B. Bird, G. Cinque, K. Curtis, J. Dorney, K. Esmonde-White, N. J Fullwood, B. Gardner, P. L Martin-Hirsch, M. J Walsh, M. R McAinsh, N. Stone, and F L Martin, “Using Raman spectroscopy to characterize biological materials,” Nat. Protoc. 11, 664-687 (2016).
[30] D. W. Shipp, F. Sinjab, and I. Notingher, “Raman spectroscopy: techniques and applications in the life sciences,” Adv. Opt. Photon. 9, 315-428 (2017).
[31] Z. Ren, Z. Xu, and E. Y. Lam, “Learning-based nonparametric autofocusing for digital holography,” Optica 5, 337-344 (2018).
[32] T. Pitkäaho, A. Manninen, and T. J. Naughton, “Focus prediction in digital holographic microscopy using deep convolutional neural networks,” Appl. Opt. 58, A202-A208 (2019).
[33] C. H. Wu, X. J. Lai, C. J. Cheng, Y. C. Yu, and C. Y. Chang, “Applications of digital holographic microscopy in therapeutic evaluation of Chinese herbal medicines,” Appl. Opt. 53, G192-G197 (2014).
[34] E. Abbe, “Beiträge zur Theorie des Mikroskops und der mikroskopischen Wahrnehmung,” Arch. mikrosk. Anat. Entwichlungsmech 9, 413-468 (1873).
[35] M. Kim, Y. Choi, C. F. Yen, Y. Sung, R. R. Dasari, M. S. Feld, and W. Choi, “High-speed synthetic aperture microscopy for live cell imaging,” Opt. Lett. 36(2), 148-150 (2011).
[36] Y. C. Lin, H. Y. Tu, X. R. Wu, X. J. Lai, and C. J. Cheng, “One-shot synthetic aperture digital holographic microscopy with non-coplanar angular-multiplexing and coherence gating,” Opt. Express 26, 12620-12631 (2018).
[37] P. Gao and C. Yuan, “Resolution enhancement of digital holographic microscopy via synthetic aperture: a review,” Light: Adv. Manuf. 3, 1–16 (2022).
[38] X. J. Lai, H. Y. Tu, C. H. Wu, Y. C. Lin, and C. J. Cheng, “Resolution enhancement of spectrum normalization in synthetic aperture digital holographic microscopy,” Appl. Opt. 54, A51-A58 (2015).
[39] M. Kim, Y. Choi, C. Fang-Yen, Y. Sung, R. R. Dasari, M. S. Feld, and W. Choi, “High-speed synthetic aperture microscopy for live cell imaging,” Opt. Lett. 36, 148-150 (2011).
[40] B. Gul, S. Ashraf, S. Khan, H. Nisar, and Iftikhar Ahmad, “Cell refractive index: Models, insights, applications and future perspectives,” Photodiagn. Photodyn. Ther. 33,102096 (2021).
[41] R. Horstmeyer, J. Chung, X. Ou, G. Zheng, and C. Yang, “Diffraction tomography with Fourier ptychography,” Optica 3, 827-835 (2016).
[42] C. Kirisits, M. Quellmalz, M. Ritsch-Marte, O. Scherzer, E. Setterqvist, and G. Steidl, “Fourier reconstruction for diffraction tomography of an object rotated into arbitrary orientations,” Inverse Problems 37, 115002 (2021).
[43] Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photon. 7, 113-117 (2013).
[44] S. S. Kou, and C. J. R., “Sheppard, Image formation in holographic tomography,” Opt. Lett. 33, 2362-2364 (2008).
[45] V. Balasubramani, H. Y. Tu, X. J. Lai, and C. J. Cheng, “Adaptive wavefront correction structured illumination holographic tomography,” Sci. Rep. 9, 10489 (2019).
[46] A. Kuś, W. Krauze, and M. Kujawińska, “Limited-angle holographic tomography with optically controlled projection generation,” Proc. SPIE 9330, 933007 (2015).
[47] F. Charriere, A. Marian, F. Montfort, J. Kuehn, T. Colomb, E. Cuche, P. Marquet, and C. Depeursinge, “Cell refractive index tomography by digital holographic microscopy,” Opt. Lett. 31, 178–180 (2006).
[48] J. Kostencka, T. Kozacki, M. Dudek, and M. Kujawińska, “Noise suppressed optical diffraction tomography with autofocus correction,” Opt. Express 22, 5731–5745 (2014).
[49] A. Kuś, W. Krauze, and M. Kujawińska, “Active limited-angle tomographic phase microscope,” J. Biomed. Opt. 20, 111216 (2015).
[50] Z. Huang and L. Cao, “Quantitative phase imaging based on holography: trends and new perspectives,” Light Sci. Appl. 13, 145 (2024).
[51] Y. C. Lin and C. J. Cheng, “Sectional imaging of spatially refractive index distribution using coaxial rotation digital holographic microtomography,” J. Opt. 16, 065401 (2014).
[52] Y. C. Lin, H. C. Chen, H. Y. Tu, C. Y. Liu, and C. J. Cheng, “Optically driven full-angle sample rotation for tomographic imaging in digital holographic microscopy,” Opt. Lett. 42, 1321-1324 (2017).
[53] D. Pirone, M. M. Villone, P. Memmolo, Z. Wang, V. Tkachenko, W. Xiao, L. Che, L. Xin, X. Li, F. Pan, P. Ferraro, P. L. Maffettone, “On the hydrodynamic mutual interactions among cells for high-throughput microfluidic holographic cyto-tomography,” Opt. Lasers Eng. 158, 107190 (2022).
[54] B. Vinoth, X. J. Lai, Y. C. Lin, H. Y. Tu, and C. J. Cheng, “Integrated dual-tomography for refractive index analysis of free-floating single living cell with isotropic superresolution,” Sci. Rep. 8, 5943 (2018).
[55] W. A. Kalender, “X-ray computed tomography,” Phys. Med. Biol. 51, R29 (2006).
[56] I. Sluimer, A. Schilham, M. Prokop, and B. van Ginneken, “Computer analysis of computed tomography scans of the lung: a survey,” IEEE Trans. Med. Imaging 25, 385-405 (2006).
[57] X. Bao and Y. Wang, “Recent Advancements in Rayleigh Scattering-Based Distributed Fiber Sensors,” Adv. Dev. Instrum. 2021, 1-17 (2021).
[58] C. C. Moura, R. S. Tare, R. O. C. Oreffo, and S. Mahajan, “Raman spectroscopy and coherent anti-Stokes Raman scattering imaging: prospective tools for monitoring skeletal cells and skeletal regeneration,” J. R. Soc. Interface 13, 20160182 (2016).
[59] M. Ayiania, E. Weiss-Hortala, M. Smith, J. S. McEwen, M. Garcia-Perez, “Microstructural analysis of nitrogen-doped char by Raman spectroscopy: Raman shift analysis from first principles,” Carbon 167, 559-574 (2020).
[60] J. D. Gelder, K. D. Gussem, P. Vandenabeele, and L. Moens, “Reference database of Raman spectra of biological molecules,” J. Raman Spectrosc. 38, 113-1147 (2007).
[61] K. Kajimoto, T. Kikukawa, H. Nakashima, H. Yamaryo, Y. Saito, T. Fujisawa, M. Demura, and M. Unno, “Transient Resonance Raman Spectroscopy of a Light-Driven Sodium-Ion-Pump Rhodopsin from Indibacter alkaliphilus,” J. Phys. Chem. B 121, 4431–4437 (2017).
[62] R. S. Jakubek, J. Handen, S. E. White, S. A. Asher, and I. K. Lednev, “Ultraviolet resonance Raman spectroscopic markers for protein structure and dynamics,” Trac trend. Anal. Chem. 103, 223-229 (2018).
[63] F. Madonini and F. Villa, “Single Photon Avalanche Diode Arrays for Time-Resolved Raman Spectroscopy,” Sensors 21, 4287 (2021).
[64] A. D. Angelis, S. Managò, M. A. Ferrara, M. Napolitano, G. Coppola, and A. C. D. Luca, “Combined Raman Spectroscopy and Digital Holographic Microscopy for Sperm Cell Quality Analysis,” J. Spectrosc. 2017, 9876063 (2017).
[65] T. A. Brunner, “Impact of lens aberrations on optical lithography,” IBM J. Res. Dev. 41, 57-67 (1997).
[66] J.S. McLellan, P.M. Prieto, S. Marcos, and S.A. Burns, “Effects of interactions among wave aberrations on optical image quality,” Vision Res. 46, 3009-3016 (2006).
[67] K. Niu and C. Tian, “Zernike polynomials and their applications,” J. Opt. 24, 123001 (2022).
[68] C. McAlinden, Mark McCartney, and Jonathan Moore, “Mathematics of Zernike polynomials: a review,” Clin. Exp. Ophthalmol. 39, 820-827 (2011).
[69] D. Zhang, J. Fan, H. Zhao, X. Lu, S. Liu, and L. Zhong, “Error evaluation for Zernike polynomials fitting based phase compensation of digital holographic microscopy,” Optik 125, 5148-5152 (2014).
[70] V. N. Mahajan and G. Dai, “Orthonormal polynomials in wavefront analysis: analytical solution,” J. Opt. Soc. Am. A 24, 2994-3016 (2007).
[71] C. Zuo, Q. Chen, W. Qu, and A. Asundi, “Phase aberration compensation in digital holographic microscopy based on principal component analysis,” Opt. Lett. 38, 1724-1726 (2013).
[72] T. Nguyen, V. Bui, V. Lam, C. B. Raub, L. C. Chang, and G. Nehmetallah, “Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection,” Opt. Express 25, 15043-15057 (2017).
[73] D. N. Deng, J. Z. Peng, W. J. Qu, Y. Wu, X. L. Liu, W. Q. He, and X. Peng, “Simple and flexible phase compensation for digital holographic microscopy with electrically tunable lens,” Appl. Opt. 56, 6007-6014 (2017).
[74] I. Choi, K. Lee, and Y. K. Park, “Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration,” Opt. Express 25, 30771-30779 (2017).
[75] Y. Deng, C. H. Huang, B. Vinoth, D. Chu, X. J. Lai, and C. J. Cheng, “A compact synthetic aperture digital holographic microscope with mechanical movement-free beam scanning and optimized active aberration compensation for isotropic resolution enhancement,” Opt. Lasers Eng. 134, 106251 (2020).
[76] L. C. Lin, C. H. Huang, Y. F. Chen, D. Chu, C. J. Cheng, “Deep learning-assisted wavefront correction with sparse data for holographic tomography,” Opt. Lasers Eng. 154, 107010 (2022).
[77] D. Maji, P. Sigedar, and M. Singh, “Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors,” Biomed. Signal Process. Control 71, 103077 (2022).
[78] N. Ibtehaz and M. S. Rahman, “MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation,” Neural Netw. 121, 74-87 (2020).
[79] S. Lathuilière, P. Mesejo, X. Alameda-Pineda, and R. Horaud, “A Comprehensive Analysis of Deep Regression,” IEEE Trans. Pattern Anal. Mach. Intell. 42, 2065-2081 (2019).
[80] L. Palagi, “Global optimization issues in deep network regression: an overview,” J. Glob. Optim. 73, 239-277 (2019).
[81] Q. Li, R. Luo, H. F. Chen, “Dynamical important residue network (DIRN): network inference via conformational change,” Bioinformatics 35, 4664-4670 (2019).
[82] C. Peng, Y. Liu, X. Yuan, and Q. Chen, “Research of image recognition method based on enhanced inception-ResNet-V2,” Multimed. Tools Appl. 81, 34345-34365 (2022).
[83] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning,” arXiv:1602.07261.
[84] Y. Al-Kofahi, W. Lassoued, W. Lee, and B. Roysam, “Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images,” IEEE Trans. Biomed. Eng. 57, 841-852 (2010).
[85] C. F. Koyuncu, S. Arslan, I. Durmaz, R. Cetin-Atalay, C. Gunduz-Demir, “Smart Markers for Watershed-Based Cell Segmentation,” PLoS ONE 7, e48664 (2012).
[86] C. Molnar, I. H. Jermyn, Z. Kato, V. Rahkama, P. Östling, P. Mikkonen, V. Pietiäinen, and P. Horvath, “Accurate Morphology Preserving Segmentation of Overlapping Cells based on Active Contours,” Sci. Rep. 6, 32412 (2016).
[87] E. Meijering, “Cell Segmentation: 50 Years Down the Road [Life Sciences],” IEEE Signal Process Mag. 29, 140-145, (2012).
[88] F. H. D. Araújo, R. R. V. Silva, D. M. Ushizima, M. T. Rezend, C. M. Carneiro, A. G. C. Bianchi, F. N. S. Medeiros, “Deep learning for cell image segmentation and ranking,” Comput. Med. Imaging Graph. 72, 13-21 (2019).
[89] Y. Al-Kofahi, A. Zaltsman, R. Graves, W. Marshall, and M. Rusu, “A deep learning-based algorithm for 2-D cell segmentation in microscopy images,” BMC Bioinformatics 19, 365 (2018).
[90] P. Agrawal, N. Katal, and N. Hooda, “Segmentation and classification of brain tumor using 3D-UNet deep neural networks,” Int. J. Cogn. Comput. Eng. 3, 199-210 (2022).
[91] S. Qamar, H. Jin, R. Zheng, P. Ahmad, and M. Usama, “A variant form of 3D-UNet for infant brain segmentation,” Future Gener. Comput. Syst. 108, 613-623 (2020).
[92] D. Müller, I. Soto-Rey, and F. Kramer, “Towards a guideline for evaluation metrics in medical image segmentation,” BMC Res. Notes 15, 210 (2022).
[93] F. Hou, W. Lei, S. Li, J. Xi, M. Xu, J. Luo, “Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation,” Autom. Constr. 121, 103414 (2021).
[94] R. E. Ellis, J. Yuan, and H. R. Horvitz, “Mechanisms and Functions of Cell Death,” Annu. Rev. Cell Biol. 7, 663–698 (1991).
[95] D. L. Vaux and S. J. Korsmeyer, “Cell Death in Development,” Cell 96, 245–254 (1999).
[96] D. R. Green, “The Coming Decade of Cell Death Research: Five Riddles,” Cell 177, 1094–1107 (2019).
[97] G. Kroemer, P. Petit, N. Zamzami, J. L. Vayssière, and B. Mignotte, “The biochemistry of programmed cell death,” The FASEB J. 9, 1277-1287 (1995).
[98] K. C. Zimmermann, C. Bonzon, and D. R. Green, “The machinery of programmed cell death,” Pharmacol. Ther. 92, 57-70 (2001).
[99] M. D Jacobson, M. Weil, and M. C Raff, “Programmed Cell Death in Animal Development,” Cell 88, 347–354 (1997).
[100] A. P. B. d. Silva, A. Pollett, S. R. Rittling, D. T. Denhardt, J. Sodek, and R. Zohar, “Exacerbated tissue destruction in DSS-induced acute colitis of OPN-null mice is associated with downregulation of TNF-α expression and non-programmed cell death,” J. Cell. Physiol. 208, 629-639 (2006).
[101] G. Yan, M. Elbadawi, and T. Efferth, “Multiple cell death modalities and their key features (Review),” World Acad. Sci. J. 2, 39-48 (2020).
[102] N. Mizushima, “Autophagy: process and function,” Genes Dev. 21, 2861-2873 (2007).
[103] I. Tanida, T. Ueno, and E. Kominami, “LC3 and Autophagy,” Methods Mol. Biol. 445, 77–88 (2008).
[104] M. T. J. Halma, P. E. Marik, and Y. M. Saleeby, “Exploring autophagy in treating SARS-CoV-2 spike protein-related pathology,” Endocr. Metab. Sci. 14, 100163 (2024).
[105] M. O. Hengartner, “The biochemistry of apoptosis,” Nature 407, 770-776 (2000).
[106] S. W. Lowe and A. W. Lin, “Apoptosis in cancer,” Carcinogenesis 21, 485-495 (2000).
[107] Z. Asadzadeh, E. Safarzadeh, S. Sagaei, A. Baradaran, A. Mohammadi, K. Hajiasgharzadeh, A. Derakhshani, A. Argentiero, N. Silvestris, and B. Baradaran, “Current Approaches for Combination Therapy of Cancer: The Role of Immunogenic Cell Death,” Cancers 12, 1047 (2020).
[108] J. Li, F. Cao, H. Yin, Z. Huang, Z. Lin, N. Mao, B. Sun, and G. Wang, “Ferroptosis: past, present and future,” Cell Death Dis. 11, 88 (2020).
[109] B. Liu, W. Wang, A. Shah, M. Yu, Y. Liu, L. He, J. Dang, L. Yang, M. Yan, Y. Ying, Z. Tang, and K. Liu, “Sodium iodate induces ferroptosis in human retinal pigment epithelium ARPE-19 cells,” Cell Death Dis. 12, 230 (2021).
[110] M. Dodson, R. Castro-Portuguez, D. D. Zhang, “NRF2 plays a critical role in mitigating lipid peroxidation and ferroptosis,” Redox Biol. 23, 101107 (2019).
[111] C. H. Huang, Y. J. Lai, L. N. Chen, Y. H. Hung, H. Y. Tu, and C. J. Cheng, “Label-Free Three-Dimensional Morphological Characterization of Cell Death Using Holographic Tomography,” Sensors 24, 3435 (2024)
[112] Y. Kumamoto, Y. Harada, H. Tanaka, and T. Takamatsu, “Rapid and accurate peripheral nerve imaging by multipoint Raman spectroscopy,” Sci. Rep. 7, 845 (2017).
[113] K. Shin and H. Chung, “Wide area coverage Raman spectroscopy for reliable quantitative analysis and its applications,” Analyst 138, 3335-3346 (2013).
[114] Paul L. Stiles, Jon A. Dieringer, Nilam C. Shah, and Richard P. Van Duyn, “Surface-Enhanced Raman Spectroscopy,” Annu. Rev. Anal. Chem. 1, 601-626 (2008).
[115] H. Chang, H. Kang, S. Jeong, E. Ko, Y. S. Lee, H. Y. Lee, and D. H. Jeong, “A fast and reliable readout method for quantitative analysis of surface-enhanced Raman scattering nanoprobes on chip surface,” Rev. Sci. Instrum. 86, 055004 (2015).
[116] Lingbo Kong and James Chan, “A Rapidly Modulated Multifocal Detection Scheme for Parallel Acquisition of Raman Spectra from a 2-D Focal Array,” Anal. Chem. 86, 13, 6604–6609 (2014).
[117] F. Sinjab, Z. Liao, and I. Notingher, “Applications of Spatial Light Modulators in Raman Spectroscopy,” Appl. Spectrosc. 73, 727-746 (2019).
[118] O. Ripoll, V. Kettunen, and H. P. Herzig, “Review of iterative Fourier-transform algorithms for beam shaping applications,” Opt. Eng. 43, 2549–2555 (2004).
[119] D. Y. Alsaka, Ç. Arpali, and S. A. Arpali, “A comparison of iterative Fourier transform algorithms for image quality estimation,” Opt Rev 25, 625–637 (2018).
[120] J. W. Goodman, Introduction to Fouri er Optics (Roberts & Company, 2004).
[121] S. Djidel, J. K Gansel, H. I Campbell, and A. H Greenaway, “High-speed, 3-dimensional, telecentric imaging,” Opt. Express 14, 8269-8277 (2006).
[122] Nanolive company, 3D CELL EXPLORER-fluo product. (Accessed: 2024) https://www.nanolive.ch/products/3d-microscopes/fluo/
[123] Tomocube company, HT-2H product. (Accessed: 2024) https://tomocube.com/tomocube/view/products/product_2h