近年來，光學顯微技術成為神經科學中不可或缺的工具之一，同時藉由鈣離子標定技術，研究人員可以觀測神經細胞的活動，進而深入瞭解神經網路的交互作用。然而，光學顯微鏡所產生的影像往往受到多種因素的干擾，例如系統誤差、偽影和光源穩定性等，從而影響影像品質和分析結果的準確性。因此，如何有效地處理這些影像干擾因素，成為神經網路分析的首要前置作業。
本研究旨在建立一套通用影像處理系統平台，可對高速雙光子顯微鏡所拍攝的影像進行系統誤差修正、偽影修正、影像品質提升及數據前處理等功能。使用者可根據自己的需要選擇所需的功能，實現快速且準確的影像處理，進一步提高數據分析的準確性和穩定性。
本論文可分為兩大部分，一是影像修正及數據標準化，其中包括對雙光子顯微平台使用的掃描器偽影、系統誤差進行修正，及消除活體在拍攝過程中運動所造成的影像位移，也引入了合適的數據標準化，提供不同組實驗數據的可比較性。另一部分則是針對影像品質的提升，本研究基於Noise2Noise的概念，使用3D U-Net深度學習網路架構，實現在功能性螢光影像的去噪處理。最後實際處理不同平台所取的兩組生物樣本，呈現我們的影像處理系統作用在不同種類資料的泛用性。

In recent years, optical microscopy techniques have become crucial tools in neuroscience, enabling the observation of neuronal activity through calcium labeling. However, optical microscope images are often affected by interference factors like system errors, artifacts, and light source instability, impacting image quality and data accuracy. This study aims to create a versatile image processing platform for high-speed two-photon microscopy. Users can select specific functions to perform system error correction, artifact removal, image enhancement, and data preprocessing, improving data analysis accuracy and stability.
The thesis comprises two key components: image correction and data standardization, addressing common scanner artifacts, system errors, motion-induced image shifts, and introducing data standardization for cross-comparability. The second part focuses on image quality enhancement using the Noise2Noise concept and a 3D U-Net deep learning network for noise reduction in functional fluorescence images. Ultimately, the platform proves effective for processing two sets of biological samples from different platforms, highlighting its applicability across diverse data types.

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 2
1.2.1 螢光指示劑與雙光子顯微技術 2
1.2.2 影像優化問題 3
1.3 雙光子顯微影像平台 3
1.4 論文架構 5
第二章 系統校正與數據前處理 6
2.1 系統校正 6
2.1.1 高速掃描振鏡系統修正 6
2.1.2 快速變焦透鏡系統修正 8
2.1.3 時間校準 9
2.2 數據前處理 10
2.2.1 移動校正 10
2.2.2 感興趣區域劃分 11
2.2.3 數據標準化 13
第三章 影像實時去噪 15
3.1 Noise2Noise 15
3.2 DeepCAD-RT 17
3.3 DeepCAD-Z 18
第四章 實驗結果 20
4.1 實驗數據介紹 20
4.1.1 螢光小球 20
4.1.2 視交叉上核腦區 20
4.1.3 小腦腦區 21
4.2 Galvo系統修正結果 22
4.2.1 評估指標 22
4.2.2 修正結果呈現 25
4.2.3 修正結果討論 25
4.3 時間校準結果 26
4.3.1 模擬影像 26
4.3.2 時間校準結果與討論 26
4.4 ROI選取效果評估 30
4.4.1 評估指標 30
4.4.2 點偵測法結果呈現 31
4.4.3 U-Net細胞分割結果呈現 33
4.4.4 點偵測法與U-Net細胞分割結果討論 36
4.5 影像去噪效果評估 37
4.5.1 評估方法與指標 37
4.5.2 DeepCAD-RT去噪效果呈現 38
4.5.3 DeepCAD-Z去噪效果呈現 41
4.5.4 DeepCAD-RT與DeepCAD-Z之比較及討論 43
4.6 實際資料處理流程 45
4.6.1 SCN數據處理流程 45
4.6.2 小腦數據處理流程 46
第五章 總結 48
參考文獻 49

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