研究生: |
戚瀚文 Chi, Han-Wen |
---|---|
論文名稱: |
深度學習輔助全像斷層三維影像分割及資料視覺化 Deep learning–assisted three-dimensional segmentation for data visualization of holographic tomography |
指導教授: |
鄭超仁
Cheng, Chau-Jern 杜翰艷 Tu, Han-Yen |
口試委員: |
林立謙
Lin, Li-Chien 林昱志 Lin, Yu-Chih 杜翰艷 Tu, Han-Yen 鄭超仁 Cheng, Chau-Jern |
口試日期: | 2023/10/27 |
學位類別: |
碩士 Master |
系所名稱: |
光電工程研究所 Graduate Institute of Electro-Optical Engineering |
論文出版年: | 2023 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 58 |
中文關鍵詞: | 全像斷層 、三維細胞影像分割 、深度學習 、RGB全像顯示 、資料視覺化 |
英文關鍵詞: | holographic tomography, three-dimensional cell images segmentation, deep learning, RGB holographic display, data visualization |
研究方法: | 實驗設計法 |
DOI URL: | http://doi.org/10.6345/NTNU202301828 |
論文種類: | 學術論文 |
相關次數: | 點閱:98 下載:0 |
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本研究主要探討如何將全像斷層造影系統所擷取的三維細胞影像進行分割,得到不同的細胞胞器三維模型,並且使用深度學習來輔助快速且自動化處理。此外,本研究將會進一步把分割好的影像編寫成電腦全像片,並會詳細說明設計三維電腦全像片演算法的原理以及實現方法,最後,將運用RGB全像顯示技術,以進行光學重建實現資料視覺化的呈現。
This research primarily explores how to do the holographic tomography systems to captured three-dimensional cell images for segmentation to obtain distinct 3D models of cell organelles. It utilizes deep learning-assisted for fast and automated processing. Additionally, this research will further convert the segmented images into computer-generated holograms. The principles and implementation methods of the 3D computer-generated hologram algorithm will be elaborated upon. Finally, the RGB holographic display technique will be employee for optical reconstruction to achieve data visualization presentation.
[1] D. Gabor, “A new microscopic principle,” Nature 161, 777–778 (1948).
[2] E. N. Leith, and J. Upatnieks, “Reconstructed Wavefronts and Communication Theory,” J. Opt. Soc. Am. 52, 1123-1130 (1962).
[3] J. W. Goodman, and R. W. Lawrence, “Digital Image Formation from Electronically Detected Holograms,” Appl. Phys. Lett. 11, 77 (1967).
[4] E. Cuche, P. Marquet, and C. Depeursinge, “Simultaneous amplitude-contrast and quantitative phase-contrast microscopy by numerical reconstruction of Fresnel off-axis holograms,” Appl. Opt. 38, 6994-7001 (1999).
[5] L. Martínez-León, G. Pedrini, and W. Osten, “Applications of short-coherence digital holography in microscopy,” Appl. Opt. 44, 3977-3984 (2005).
[6] W. Choi, C. Fang-Yen, K. Badizadegan, S. Oh, N. Lue, R. R Dasari, and M. S Feld, “Tomographic phase microscopy” Nat. Methods. 9, 717-719 (2007).
[7] B. Simon, M. Debailleul, M. Houkal, C. Ecoffet, J. Bailleul, J. Lambert, A. Spangenberg, H. Liu, O. Soppera, and O. Haeberlé, “Tomographic diffractive microscopy with isotropic resolution,” Optica 4, 460-463 (2017)
[8] C. Park, S. Shin, and Y. K. Park, “Generalized quantification of three-dimensional resolution in optical diffraction tomography using the projection of maximal spatial bandwidths,” J. Opt. Soc. Am. A 35, 1891-1898 (2018)
[9] 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).
[10] A. J. Lee, D. Yoon, S. Y. Han, H. Hugonnet, W. Park, J. K. Park, Y. K. Nam, and Y. K. Park, “Label-free monitoring of 3D cortical neuronal growth in vitro using optical diffraction tomography,” Biomed. Opt. Express 12, 6928-6939 (2021).
[11] S. A. Yang, J. Yoon, K. Kim, and Y. K. Park, “Measurements of morphological and biophysical alterations in individual neuron cells associated with early neurotoxic effects in Parkinson's disease,” Cytometry A. 91, 510-518 (2017).
[12] D. Kim, S. Lee, M. Lee, J. Oh, S. A. Yang, and Y. K. Park, “Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging,” Adv Exp Med Biol. 1310. 211-238 (2021).
[13] Y. J. Jo, H. Cho, W. S. Park , G. Kim, D. H. Ryu, Y. S. Kim, M. Lee, S. Park, M. J. Lee, H. Joo, H. H. Jo, S. Lee, S. Lee, H. S. Min, W. D. Heo, Y. K. Park, “Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning,” Nat Cell Biol. 23, 1329-1377 (2021).
[14] A. A. Sekh, I. S. Opstad, G. Godtliebsen, Å. B. Birgisdottir, B. S. Ahluwalia, K. Agarwal, and D. K. Prasad, “Physics-based machine learning for subcellular segmentation in living cells,” Nat Mach Intell 3, 1071–1080 (2021).
[15] Y. Tokuoka, T. G. Yamada, D. Mashiko, Z. Ikeda, N. F. Hiroi, T. J. Kobayashi, K. Yamagata, and A. Funahashi, “3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis,” npj Syst Biol Appl 6, 32 (2020).
[16] T. H. Jeong, "Cylindrical Holography and Some Proposed Applications," J. Opt. Soc. Am. 57, 1396-1398 (1967).
[17] Y. Sando, K. Satoh, T. Kitagawa, M. Kawamura, D. Barada, and T. Yatagai, “Super-wide viewing-zone holographic 3D display using a convex parabolic mirror,” Sci Rep 8, 11333 (2018).
[18] Y. Sando, K. Satoh, D. Barada, and T. Yatagai, “Holographic augmented reality display with conical holographic optical element for wide viewing zone,” Adv. Manuf. 3, 26-34 (2022).
[19] S. Yamada, T. Kakue, T. Shimobaba, and T. Ito, “Interactive Holographic Display Based on Finger Gestures,” Sci Rep 8, 2010 (2018).
[20] A. Maimone, A. Georgiou, and J. S. Kollin, “Holographic near-eye displays for virtual and augmented reality,” ACM Trans. Graph. 36, 1-16 (2017).
[21] E. Bruckheimer, C. Rotschild, T. Dagan, G. Amir, A. Kaufman, S. Gelman, and E. Birk, “Computer-generated real-time digital holography: first time use in clinical medical imaging,” Eur. Heart J. Cardiovasc. Imaging. 17, 845-849 (2016).
[22] Y. Narita, S. Tsukagoshi, M. Suzuki, Y. Miyakita, M. Ohno, H. Arita, Y. Saito, Y. Kokojima, N. Watanabe, N. Moriyama, and S. Shibui, “Usefulness of a glass-free medical three-dimensional autostereoscopic display in neurosurgery,” Int J Comput Assist Radiol Surg. 9, 905-911 (2014).
[23] S. K. Srivastava, M. Medina-Sánchez, and B. Koch, O. G Schmidt, “Medibots: Dual-Action Biogenic Microdaggers for Single-Cell Surgery and Drug Release,” Adv Mater. 28, 832-807 (2016).
[24] X. Zhao, Y. Shi, T. Pan, D. Lu, J. Xiong, B. Li, and H. Xin, “In Situ Single-Cell Surgery and Intracellular Organelle Manipulation Via Thermoplasmonics Combined Optical Trapping,” Nano Lett. 22, 402-410 (2022).
[25] T. G. Debelee, F. Schwenker, S. Rahimeto, and D. Yohannes, CVM. 5, 347-361 (2019).
[26] S. Lou, L. Pagani, W. Zeng, X. Jiang, and P.J. Scott, “Watershed segmentation of topographical features on freeform surfaces and its application to additively manufactured surfaces,” Precis. Eng. 63, 177-186 (2020).
[27] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models” IJCV. 1, 321-331 (1988).
[28] L. C. Lin, C. H. Huang, Y. F. Chen, D. Chu, and C. J. Cheng, “Deep learning-assisted wavefront correction with sparse data for holographic tomography,” 154, 107010 (2022).
[29] S. S. Kou, and C. J. R. Sheppard, “Image formation in holographic tomography,” Opt Lett. 33, 2362-2364 (2008).
[30] E. Welling, “CONVOLUTIONAL NEURAL NETWORKS IN AUTONOMOUS VEHICLE CONTROL SYSTEMS,” Computer Science (2017).
[31] O. Ronneberger, P. Fischer, and T. Brox. “U-Net: Convolutional Networks for Biomedical Image Segmentation,” MICCAI 234-241 (2015).
[32] J. Long, E. Shelhamer, and Tr. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” CVPR 3431-3440 (2015).
[33] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation,” MICCAI 9901, 424-432 (2016).
[34] Q. Huang, J. Sun, H. Ding, X. Wang, and G. Wang, “Robust liver vessel extraction using 3D U-Net with variant dice loss function,” Comput. Biol. Med. 101, 153-162 (2018).
[35] D. Müller, I. S. Rey, and F. Kramer, “Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net,” ACS 25, (2020).
[36] I. H. Li, J. H. Shih, T. Y. Yeh, H. C. Lin, M. H. Chen, and Y. S. Huang, “Lysosomal Dysfunction and Autophagy Blockade Contribute to MDMA-Induced Neurotoxicity in SH-SY5Y Neuroblastoma Cells,” Chem. Res. Toxicol. 33, 903-914 (2020).
[37] H. Wang, Y. Xu, J. Yan, X. Zhao, X. Sun, Y. Zhang, and J. Guo, C. Zhu, “Acteoside protects human neuroblastoma SH-SY5Y cells against β-amyloid-induced cell injury,” Brain Res. 1283, 139-147 (2009).
[38] M. Sezgin, and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electronic Imaging 13, 146-168 (2004).
[39] D. Pi, J. Liu, and Y. Wang, “Review of computer-generated hologram algorithms for color dynamic holographic three-dimensional display,” Light Sci Appl 11, 231 (2022).
[40] R. W. Gerchberg, “A practical algorithm for the determination of phase from image and diffraction plane pictures,” Optik 35, 237-246. (1972).
[41] P. Sun, S. Chang, S. Liu, X. Tao, C. Wang, and Z. Zheng, “Holographic near-eye display system based on double-convergence light Gerchberg-Saxton algorithm,” Opt. Express 26, 10140-10151 (2018).
[42] R. Di Leonardo, F. Ianni, and G. Ruocco, “Computer generation of optimal holograms for optical trap arrays,” Opt. Express 15, 1913-1922 (2007).
[43] T. Haist, M. Schönleber, and H.J. Tiziani, “Computer-generated holograms from 3D-objects written on twisted-nematic liquid crystal displays,” Opt. Commun. 140, 299-3008 (1997).