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研究生：	胡瑜凌 Hu, Yvonne Yu-Ling
論文名稱：	基於深度學習還原與預測快速且清晰之深層時域聚焦多光子顯微生物影像 Fast and Clear Temporal Focusing Multiphoton Microscopy for Bioimaging via Deep Learning-Based Restoration and Extended Depth Prediction
指導教授：	陳顯禎 Chen, Shean-Jen
學位類別：	博士 Doctor
系所名稱：	理學院 - 光電科學與工程學系 Department of Photonics
論文出版年：	2023
畢業學年度：	111
語文別：	英文
論文頁數：	82
中文關鍵詞：	時域聚焦顯微術、結構照明、希爾伯特-黃轉換、U-Net 、多階段影像還原、跨模態影像對位、深度影像預測、果蠅蕈狀體
外文關鍵詞：	temporal focusing microscopy, structured illumination, Hilbert-Huang transform, U-Net, multi-stage image restoration, cross-modality image registration, deep image prediction, drosophila mushroom body
ORCID：	0000-0002-5297-913X
相關次數：	點閱：126 下載：0
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時域聚焦多光子顯微術（temporal focusing multiphoton microscopy，TFMPM）是一種具有廣視域的多光子成像技術，可廣泛應用於高速獲取生物樣本的高解析度影像。然而，在面照明下，TFMPM影像常常受到固有雜訊、失真和低訊噪比的影響，降低影像的精確度並妨礙後續的訊號分析。本論文首先利用傅立葉光學模擬TFMPM成像過程，以推斷透過結構化照明進行訊號調變的最佳空間頻率，並採用了希爾伯特-黃轉換（Hilbert-Huang transform，HHT）方法，透過兩張在相同空間頻率下相位差為180度的TFMPM影像，還原成一張完整的影像，藉由減少TFMPM影像的軸向激發限制來提高訊躁比，同時避免由數位微型鏡裝置（digital micromirror device）提供的離散模式所引起的殘留圖案。然而，利用HHT重建影像會使得原本TFMPM的影像取得速度減半，進而失去其優勢。
論文進一步提出了基於U-Net的多階段（multi-stage）深度學習架構，透過多個功能塊，用於逐步改善恢復影像的品質。實驗結果證明了所提方法的有效性。透過使用雙階段U-Net進行體外TFMPM影像還原實驗，在深層獲得的影像中顯著改善了結構相似度（structural similarity index，SSIM）。還原過程的第二階段提供了增量的增強效果，使得影像的特徵正加突出，且明顯改善影像對比度。透過使用三階段U-Net進行活體TFMPM影像還原，凸顯了低訊躁比輸入影像所帶來的挑戰。藉由轉移學習，還原的準確性可獲得大幅的提升，從而改善了結構形態和局部強度分佈。
此外，本論文提出了一種基於循環神經網絡（recurrent neural network）和卷積長短期記憶機制（convolutional long short-term memory）的深度學習物理預測網絡，用於深層影像預測以推斷TFMPM無法獲得的圖像。該網絡通常運用在影片，藉由物理模型推斷影片中物體的趨勢，來預測未來的影像。初步結果顯示預測網絡雖然能夠產生非常接近標準的型態，但卻無法預測出影像中的細節，如神經細胞的樣貌與位置。雖然此網路可以從恢復的TFMPM體積中推斷出深層未獲得的影像層，但是在細節預測的部分仍須進一步加強。
總結，本論文對TFMPM的數值模擬驗證和HHT在影像重建中的有效性做出了貢獻。改善了在極短時間內獲取TFMPM影像的清晰度，進而實現了在不同科學領域中對生物樣本進行更精確的分析和解釋。本研究亦提出深度學習影像還原方法，提供一個不需犧牲取像速度，且又能提高TFMPM影像品質的解決方案，從而實現對生物結構和動態過程的更精確和可靠的分析。

Temporal focusing microscopy (TFMPM) is a powerful imaging technique widely applied in various scientific fields for capturing high-resolution images of biological specimens in high acquisition rate. However, under plane illumination, TFMPM images often suffer from inherent noise, distortion, and low signal-to-noise ratio (SNR), which can hinder accurate analysis and interpretation. In this dissertation, the process of TFMPM imaging is numerically simulated using Fourier optics to infer the optimal spatial frequency for the signal modulation via structured illumination. The Hilbert-Huang transform (HHT) is employed by taking two TFMPM images under same spatial frequency with a phase difference of 180 to reconstruct the underlying signals and improve the SNR by reducing axial excitation confinement of TFMPM images, while preventing pattern residual aroused due to digitalized pattern provided by digital micromirror device. However, the HHT method compromises the speed advantage of TFMPM by a factor of 2.
A deep learning multi-stage approach, which is based on the U-Net architecture comprises various functional blocks, is proposed for image restoration to progressively refine the restored images. Experimental results demonstrate the effectiveness of the proposed approach. In-vitro TFMPM image restoration experiments using a two-stage U-Net show significant improvements in structural similarity for images obtained at deeper depths. The second stage of the restoration process provides incremental enhancements, resulting in more contextualized features and improved contrast. In-vivo TFMPM image restoration using a three-stage U-Net reveals the challenges posed by low SNR in input images. However, by employing transfer learning, the restoration accuracy is improved, leading to better morphology and local intensity distribution of structures.
In addition, this dissertation proposes a deep learning physics-based prediction network based on recurrent neural networks and convolutional long short-term memory mechanisms, which is commonly used in video applications to predict future images by inferring the trends of objects in the video based on physical models, for deep layer image prediction to infer images that cannot be obtained by TFMPM. Preliminary results show that while the prediction network can generate morphologies that are very close to the ground truth, it struggles to predict fine details such as the appearance and location of neural cells in the images. Although this network can infer the unobtained deeper layers of images from the restored TFMPM volume, further improvements are needed in the aspect of detailed prediction.
In summary, this dissertation contributes to the validation of numerical simulation of TFMPM and the effectiveness of the HHT in image reconstruction. The enhancement of TFMPM image clarity under extremely short acquisition time is addressed, enabling more precise analysis and interpretation of biological specimens across various scientific fields. Furthermore, the generation of TFMPM image in deeper layer is obtained, which shows the potential in thick tissue imaging that goes beyond the limitation of penetration depth in TFMPM.

Abstract......................................................................................................................................................I
摘要............................................................................................................................................................III
Acknowledgements.............................................................................................................................V
List of Figures......................................................................................................................................VIII
Abbreviations.....................................................................................................................................XIII
Chapter 1 Introduction.......................................................................................................................1
          1-1	Trajectory of development in two-photon excitation microscopy...........1
          1-2	Toward higher acquisition rate..............................................................................2
          1-3	Toward better resolution and image quality.....................................................5
          1-4	Motivation and dissertation outlines..................................................................9
          1-5	Outlines...........................................................................................................................9
Chapter 2 Multiphoton Excitation Microscopy.......................................................................11
          2-1	Theoretical background.........................................................................................11
                  2-1-1	Nature offluorescence...................................................................................11
                  2-1-2	Physics of multiphoton absorption...........................................................14
         2-2	Point-scan two-photon excitation microscopy......................................................16
         2-3	Temporal focusing multiphoton excitation microscopy....................................18
                  2-3-1	Concept...............................................................................................................18
                  2-3-2	Diffraction element.........................................................................................20
                  2-3-3	Optical configuration.....................................................................................21
Chapter 3 Denoise of Temporal Focusing via Structured Illumination..........................24
         3-1	Numerical simulation of temporal focusing...........................................................24
         3-2	Verification of simulation model.................................................................................26
         3-3	Modulation of spatial frequency and orientation in temporal focusing.....28
         3-4	Verification of AEC improvement................................................................................31
         3-5	Image reconstruction via Hilbert-Huang transform............................................32
3-5-1	Hilbert-Huang transform...............................................................................................33
3-5-2	Image reconstruction via Hilbert-Huang transform............................................36
Chapter 4 Cross-Modality Image restoration for Temporal Focusing Microscopy via Deep Learning.....................................................................................................................................39
         4-1	Deep learning in image restoration...........................................................................39
         4-2	Drosophila mushroom body.........................................................................................42
                  4-2-1	In-vitro sample preparation.........................................................................43
                  4-2-2	In-vivo sample preparation..........................................................................44
         4-3	Configuration of temporal focusing multiphoton microscopy for eGFP-expressed drosophila........................................................................................................................45
         4-4	Cross-modality image registration............................................................................46
         4-5	Multi-stage 3D U-Net for image restoration.........................................................50
                  4-5-1	Introduction of functional blocks..............................................................50
                     4-5-1-1	Efficient channel attention..................................................................50
                     4-5-1-2	Half instance normalization module...............................................51
                     4-5-1-3	Residual block..........................................................................................52
                     4-5-1-4	Self-supervised attention module....................................................53
                  4-5-2	Network architecture.....................................................................................54
         4-6	Experimental results.........................................................................................................57
                  4-6-1	In-vitro TFMPM image restoration via two-stage U-Net.................57
                  4-6-2	Fast and clear in-vivo MB restoration via three-stage U-Net.........59
Chapter 5 Extended Depth Prediction........................................................................................63
         5-1	The basis of PhyDNet prediction network...............................................................63
         5-2	Functional blocks applied in PhyDNet......................................................................65
                  5-2-1	Convolutional long short-term memory.................................................65
                  5-2-2	PhyCell.................................................................................................................66
                  5-2-3	Convolutional block attention module...................................................67
                  5-2-4	Aphynity...............................................................................................................69
         5-3	Network architecture for image prediction in deeper layer..............................70
         5-4	Experimental results.........................................................................................................70
Chapter 6 Conclusions......................................................................................................................72
Bibliography..........................................................................................................................................74
                                    

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