高通量計算顯微影像系統之研製於生物醫學成像與分析

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：8

、訪客IP：18.190.159.10

姓名

楊奕傑(Yi-Jie Yang) 查詢紙本館藏

畢業系所

生物醫學工程研究所

論文名稱

高通量計算顯微影像系統之研製於生物醫學成像與分析
(Research and Development of High-throughput Computational Micro-imaging System for Biomedical Imaging and Analysis)

相關論文

★ 具生物沾粘性免疫奈米磁珠之電化學平台於急性冠心病標誌物檢測	★ 離子液體應用於脂溶性蛋白之快速萃取及檢測
★ 深度學習計算成像系統於生物醫學顯微影像之重建與分析	★ 磁電化學免疫分析系統於新型冠狀病毒感染檢測之研製
★ 計算照明高通量生物醫學成像顯微鏡系統	★ 表面增強拉曼散射探針及微流道系統用於癌症細胞及外泌體表面生物標記物的多重檢測
★ 菲涅耳數位全像顯微系統於全血細胞分析之研製	★ 遮罩區域卷積類神經網路於醫學影像物件偵測分析應用
★ 基於深度學習之高通量顯微影像系統於血液分析	★ 可功能化編碼之表面增強拉曼光譜標籤探針於高靈敏與多重生醫分子檢測
★ 功能性抗生物沾黏單層膜於冠狀動脈心血管疾病標誌物之檢測應用	★ 鼻咽癌外泌小體藉由EB病毒潛伏膜蛋白1促進巨噬細胞免疫抑制型分化

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

現行光學顯微影像系統在成像上由於受限於空間帶寬乘積(Space-Bandwidth Product)
效應，因此影像解析度與成像視野範圍間相互受到限制，在影像取得上可獲得具有精細
但視野區域有限或是具有廣區域視野但較差解析度之影像，無法同時取得具高解析度與
廣視野範圍影像，為此通常需要裝載高倍率顯微鏡與機械掃描裝置組合構建，不僅增加
系統的複雜性和成本，同時限制圖像採集速度，大幅度的限制場域之使用。
本研究建立出計算顯微成像系統，成功降低了光學成像中空間帶寬乘積之限制，以
傅立葉疊層圖像(Fourier Ptychography, FP)為基礎，藉由編成激發光陣列以此提供多重角
度之光源，並經由影像感測裝置擷取多重空間角頻率之光場訊息，透過疊代傅立葉頻域
之演算過程經收斂後重建出同時具高解析度與廣視場之高通量顯微影像。並且更進一步
的，使用深度學習之技術，基於編碼與解碼(Encoder-Decoder)框架下結合平行式模型架
構，將原本傅立葉疊層圖像技術對於重建影像之大量低解析度測量影像之需求，簡化為
採集單張圖片即可完成，解決了其所造成大量時間花費於收集重建之所需影像上之冗長
影像擷取與重建過程之時間與過程。
本研究論文成功建立可同時獲得高解析度與廣視野範圍影像之計算成像系統，在生
醫相關顯微成像之應用上，有利取得於多種生理條件與環境下之巨量的細胞結構以及其
行為動力學的時間與空間資訊，達到無偏見及可系統化的生物過程並進行更進一步之分
析與評估。

摘要(英)

Due to the space-bandwidth product effect in the imaging of optical microscope systems,
the image resolution and the field of view (FOV) restrict each other. Hence, it is impossible to
obtain images with high resolution and wide FOV simultaneously. For this reason, a highmagnification objective lens and a mechanical scanning device are usually needed. This
increases the complexity and cost of the system, slows down the image acquisition speed, and
greatly limits the actual space available for the object.
In this study, we established a computational microscopy imaging system. Based on
Fourier Ptychography (FP) technology, the excitation light array was arranged to provide light
sources with multiple angles. The image sensor captures the light information of multiple
angular spatial frequencies. Through converging the information with an iterative
computational process, images with high resolution and wide FOV are reconstructed.
Furthermore, deep learning based on an encoder-decoder framework combined with a parallel
model architecture can reconstruct using a single image. Leaving out the original redundant
stack of low-resolution images reduces the time spent on image acquisition and reconstruction.
This research has successfully established a computational imaging system that can
automatically obtain high-resolution and wide-field images. When applied to microscopic
biomedical imaging, this technique is beneficial for visualizing subtle cell structures and their
dynamic behavior under various physiological conditions and environments. In addition,
achieving unbiased and systematized biological processes for further analysis and evaluation.

關鍵字(中)

★ 顯微影像
★ 計算成像系統
★ 傅立葉疊層成像
★ 深度學習

關鍵字(英)

★ Micro-imaging
★ Computational Imaging System
★ Fourier Ptychography
★ Deep Learning

論文目次

中文摘要 .................................................................................................................................i
ABSTRACT ...........................................................................................................................ii
致謝 ....................................................................................................................................... iii
目錄 ....................................................................................................................................... iv
圖目錄 ................................................................................................................................... vi
第一章緒論..........................................................................................................................1
1-1 研究方法與動機…………………………………………………………………………………………..….…1
1-2 論文架構…………………………………………………………………………………………………….………1
第二章文獻探考..................................................................................................................2
2-1 傅立葉疊層成像技術…………………………………………………………………………………………2
2-1-1 前言………………………………………………………………………………………………….………2
2-1-2 物鏡極限…………………………………………………………………………………..….………….3
2-1-3 傳統光學顯微鏡成像過程 ...........................................................................5
2-1-4 傅立葉疊層成像技術原理 ...........................................................................7
2-1-5 傅立葉疊層成像迭代原理 ...........................................................................9
2-2 人工神經網路………………………………………………………………………………………………….12
2-2-1 人工智慧 .....................................................................................................12
2-2-2 單層感知器…………………………………………………………………………..……….……….13
2-2-3 多層感知器…………………………………………………………………………………….………13
2-2-4 卷積神經網路……………………………………………………………………………….…..…..14
第三章實驗方法................................................................................................................17
3-1 研究方法與架構…………………………………………………………………………………………......17
3-2 傅立葉疊層成像技術系統架設與重建……………………………….…………………………..17
v
3-2-1 系統零件與各參數 .....................................................................................17
3-2-2 系統架設 .....................................................................................................19
3-2-3 低解析度影像擷取裝置與順序 .................................................................21
3-2-4 重建程式迭代順序 .....................................................................................23
3-3 影像重建之深度學習模型……………………………………………………………………………….24
3-3-1 影像重建之深度學習模型 .........................................................................24
3-3-2 編碼與解碼(Encoder-decoder)之應用 .......................................................24
3-3-3 平行式網路模型演算結構 ..........................................................................25
第四章實驗結果與討論 ....................................................................................................27
4-1 傅立葉疊層成像技術結果……………………………………………………………………………….27
4-1-1 資料收集 .....................................................................................................27
4-1-2 結果比較 .....................................................................................................33
4-2 深度學習之影像重建………………………………………………………………………….……………38
4-2-1 植物細胞影像資料集 ..................................................................................38
4-2-1 結構相似性指標 ..........................................................................................39
4-2-3 全卷積深度學習模型影像重建 .................................................................41
第五章結論........................................................................................................................42
5-1 結論……………………………………………………………………………………………………………….….42
參考文獻 Reference ............................................................................................................43

參考文獻

1. Lohmann, A.W., et al., Space–bandwidth product of optical signals and systems. JOSA
A, 1996. 13(3): p. 470-473.
2. Zheng, G., R. Horstmeyer, and C. Yang, Wide-field, high-resolution Fourier
ptychographic microscopy. Nature photonics, 2013. 7(9): p. 739-745.
3. Juškaitis, R., Characterizing high numerical aperture microscope objective lenses, in
Optical imaging and microscopy. 2003, Springer. p. 21-43.
4. Ou, X., et al., High numerical aperture Fourier ptychography: principle,
implementation and characterization. Optics express, 2015. 23(3): p. 3472-3491.
5. Zheng, G., X. Ou, and C. Yang, 0.5 gigapixel microscopy using a flatbed scanner.
Biomedical optics express, 2014. 5(1): p. 1-8.
6. Marchand, E., Derivation of the point spread function from the line spread function.
JOSA, 1964. 54(7): p. 915-919.
7. Rossmann, K., Point spread-function, line spread-function, and modulation transfer
function: tools for the study of imaging systems. Radiology, 1969. 93(2): p. 257-272.
8. Goodman, J. and P. Sutton, Introduction to Fourier Optics. Quantum and Semiclassical
Optics-Journal of the European Optical Society Part B, 1996. 8(5): p. 1095.
9. Zheng, G., et al., Concept, implementations and applications of Fourier ptychography.
Nature Reviews Physics, 2021. 3(3): p. 207-223.
10. Sun, J., et al., Sampling criteria for Fourier ptychographic microscopy in object space
and frequency space. Optics express, 2016. 24(14): p. 15765-15781.
11. Yeh, L.-H., et al., Experimental robustness of Fourier ptychography phase retrieval
algorithms. Optics express, 2015. 23(26): p. 33214-33240.
12. Ou, X., et al., Quantitative phase imaging via Fourier ptychographic microscopy.
Optics letters, 2013. 38(22): p. 4845-4848.
13. Tian, L., et al., Computational illumination for high-speed in vitro Fourier
ptychographic microscopy. Optica, 2015. 2(10): p. 904-911.
14. Dick, S., Artificial intelligence. 2019.
15. Cimatti, A., et al., Weak, strong, and strong cyclic planning via symbolic model checking.
Artificial Intelligence, 2003. 147(1-2): p. 35-84.
16. Jordan, M.I. and T.M. Mitchell, Machine learning: Trends, perspectives, and prospects.
Science, 2015. 349(6245): p. 255-260.
17. Shlens, J., A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100,
2014.
18. Suthaharan, S., Support vector machine, in Machine learning models and algorithms
for big data classification. 2016, Springer. p. 207-235.
44
19. Wang, S.-C., Artificial neural network, in Interdisciplinary computing in java
programming. 2003, Springer. p. 81-100.
20. Sak, H., A.W. Senior, and F. Beaufays, Long short-term memory recurrent neural
network architectures for large scale acoustic modeling. 2014.
21. Xiao, C., et al., Generating adversarial examples with adversarial networks. arXiv
preprint arXiv:1801.02610, 2018.
22. LeCun, Y. and Y. Bengio, Convolutional networks for images, speech, and time series.
The handbook of brain theory and neural networks, 1995. 3361(10): p. 1995.
23. Nguyen, T., et al., Deep learning approach for Fourier ptychography microscopy.
Optics express, 2018. 26(20): p. 26470-26484.
24. Cheng, Y.F., et al., Illumination pattern design with deep learning for single-shot
Fourier ptychographic microscopy. Optics express, 2019. 27(2): p. 644-656.
25. Zhang, J., et al., Precise brightfield localization alignment for Fourier ptychographic
microscopy. IEEE Photonics Journal, 2017. 10(1): p. 1-13.
26. Sun, J., et al., Efficient positional misalignment correction method for Fourier
ptychographic microscopy. Biomedical optics express, 2016. 7(4): p. 1336-1350.
27. Cho, K., et al., On the properties of neural machine translation: Encoder-decoder
approaches. arXiv preprint arXiv:1409.1259, 2014.
28. Dumoulin, V. and F. Visin, A guide to convolution arithmetic for deep learning. arXiv
preprint arXiv:1603.07285, 2016.
29. Rukundo, O. and H. Cao, Nearest neighbor value interpolation. arXiv preprint
arXiv:1211.1768, 2012.
30. Smith, P., Bilinear interpolation of digital images. Ultramicroscopy, 1981. 6(2): p. 201-
204.
31. Hwang, J.W. and H.S. Lee, Adaptive image interpolation based on local gradient
features. IEEE signal processing letters, 2004. 11(3): p. 359-362.
32. Channappayya, S.S., A.C. Bovik, and R.W. Heath, Rate bounds on SSIM index of
quantized images. IEEE Transactions on Image Processing, 2008. 17(9): p. 1624-1639.
33. Hore, A. and D. Ziou. Image quality metrics: PSNR vs. SSIM. in 2010 20th international
conference on pattern recognition. 2010. IEEE.

指導教授

黃貞翰(chen-han huang)

審核日期

2022-8-3

推文