在過去數十年間，機器學習與深度學習已為複雜的光譜分析以及臨床與研究應用之醫學影像分析帶來史無前例的機會。本篇論文針對以上議題進行深入探討，主要內容包含以下兩個部分：
第一部分聚焦在探討如何自難以區分辨識的體外糖水溶液之近紅外光譜（波長範圍 900 – 2200 nm）中定量得出糖水溶液之濃度。由於機器學習與深度學習已在各個領域中有了廣泛的應用，但在近紅外光譜分析方面的探討仍然不多，因此，本文將深入探究近紅外光譜分析方面的困難與挑戰。藉由各種糖水溶液樣本的濃度與其近紅外光譜的正確量測，作為機器學習與深度學習模型之訓練，以及分析辨識重要的訊號特徵，我們可達到正確的濃度預測。在迴歸問題方面，我們嘗試了數種機器學習模型，用以辨識難以區分的近紅外線光譜，從中我們得知 Support Vector Machine Regression (SVMR)、Extra Trees Regression (ETR)、以及 Principle Component Analysis Neural Network (PCA-NN) 可達到最佳的預測精準度，其 correlation coefficient R 可達 0.99 以上，而且其 determination of coefficient R2 可達0.985 以上。此外，藉由重要的訊號特徵分析，我們可以展現各模型的預測穩定性。本文的研究顯示模型的表現深受其所學習到的訊號重要特徵所影響，如果模型在不同組的訓練資料中所學習到的高比重的特徵有很大比例一致，該模型的預測表現則相對較穩定。在分類問題方面，我們實作了不同層數的 Convolutional Neural Network (CNN) 模型並連接兩層Fully Connected Neural Network 作為分類器，來進行自不同糖水溶液濃度（濃度範圍為 50 mg/dl – 430 mg/dl）中量測的近紅外光譜之多重類別（即多種水溶液之濃度）分類。我們的結果顯示，對於未知濃度的水溶液，我們的模型也可以將其歸類到最接近的濃度類別。因此，我們的模型具備相當的穩定性與預測能力。
論文的第二部分聚焦在準確地自 FF-OCT 人類皮膚活體影像中分離出紅血球細胞與微血管網路的影像。由於自動化由 FF-OCT影像中擷取出紅血球與微血管網路，對於醫師在臨床上診斷、治療、以及追蹤各種皮膚疾病的進程能帶來幫助，因此我們採用了深度學習的方法來發展此一自動化的程序。要訓練一個深度學習模型，需要大量的訓練資料，然而，要準備大量且標示過的訓練資料，卻需要耗費大量的時間與大量的專家人力。為了克服此困難，我們嘗試了數據擴充 (data augmentation) 與模擬產生資料的方式來累積足夠的訓練資料。以紅血球影像擷取而言，我們採用了數據擴充的方式，首先以人工的方式自原始 FF-OCT 影像中擷取出數個紅血球影像，其條件設定為每顆紅血球的半徑必須為 6 – 8 μm，然後再將其隨機拼貼入 OCT 背景影像中，藉此產生大量訓練資料。模型訓練完成後，我們採用「交集 / 聯集比」(mean IoU) 的方法來評估模型預測的準確性。我們的結果顯示我們的模型可以成功自真實 OCT 影像中擷取出單一紅血球影像。在另一方面，由於標示 OCT 影像中的微血管網路相對上困難許多，我們發展了一套自動化產生模擬 OCT 皮膚內微血管網路之三度空間影像，它可同時產生人工合成的類 FF-OCT 皮膚內微血管網路影像作為模型的輸入，以及其相對應的微血管網路真實分佈 (ground truth) 資料，以作為模型的訓練資料。為了評估這些合成影像的品質，我們採用了 U-Net 深度學習模型，以我們合成的影像作為訓練資料來訓練模型，並將訓練好的模型拿來測試真實 OCT 影像，看是否能正確擷取出微血管網路分佈。我們的結果顯示由人工合成的資料所訓練出的模型在擷取真實 OCT之微血管網路影像時可以達到一定的正確率，證實我們的方法可以大量產生有用的訓練資料，可用於模型訓練以及相關的研究。以此為基礎，我們可進一步改進模型，以達到精準擷取皮膚內微血管網路影像之目的。

The past few decades’ machine learning and deep learning brings unprecedented opportunities to investigate the complexity of optical spectroscopy output for spectra processing and medical image analysis for clinical and research studies. This dissertation has two major parts:
The first part of the dissertation focused on developing and exploring accurate and quantifiable sensing of in vitro glucose concentration from hardly distinguishable near-infrared (NIR) spectra with a wavelength ranging from 900-2200 nm. Despite their unprecedented advantage in various fields, both machine learning and deep learning techniques for NIR analysis remains largely unexplored. To this end, deeper understanding of NIR spectroscopy challenges have been discussed. The objectives of this part are achieved through investigating the correspondence between the accurate glucose level and the measured NIR spectra of various aqueous glucose samples via regression and classification, and the identification of major contributing features. In the case of the regression techniques, several machine learning models that addressed the hardly distinguishable NIR spectra dataset from aqueous glucose concentrations are presented. Support vector machine regression (SVMR), Extra trees regression (ETR), and principal component analysis-neural network (PCA-NN) achieved excellent performance, which showed correlation coefficient R > 0.99 and determination of coefficient R2 > 0.985. In addition, to show the robustness of each machine learning approach, the major contributing features were explored. Our results showed that the performance of the models is largely affected by the pattern of the important features learned by the models. It is found that having large overlapping of the high-weighting features learned by the model when trained by different data sets may be an indication of model stability. In the case of classification techniques, convolutional neural networks (CNN) models with different convolutional layers followed by two fully connected layers were implemented for multi-class classification of near-infrared spectra measured from glucose aqueous with various concentrations ranging from 50 mg/dl to 430 mg/dl. The result showed that the CNN with three convolutional layers can achieve a better accuracy compared to one, two, and four convolutional layers. Unknown concentrations were passed to the best performing model to validate the robustness of the proposed model. The result revealed that the model can perform the closest classification for the unknown data, which reveals the robustness and predicting power of our model.
The second part of this dissertation aimed at accurate segmentation of red blood cells (RBCs) and capillaries from full-field optical coherence tomography (FF-OCT) in vivo human skin images. Because automatic detection of RBCs and capillaries in an FF-OCT is vital for assisting doctors to diagnose, treat, and track the development of various skin diseases at early stages, we built automatic RBCs and capillaries segmentation based on a deep learning technique. Usually, a gigantic amount of labelled data is required for the successful training of deep learning model, but preparing the required amount of labelled data is highly time consuming and experts’ man-power demanding. To overcome this fundamental problem, in the former (RBC segmentation) case, data augmentation technique was employed to obtain the required number of training datasets. The performance of the proposed method was evaluated by comparing the ground truth RBCs labeled by the authors with the constraint of diameter around 6 µm to 8 µm and the deep learning prediction results using a method called mean Intersection over Union (mean IoU). The result showed that the RBCs segmentation performed by deep learning is in good agreement with the ground truth. The real tissue image was provided to the proposed model and it accurately segmented the RBC. Thus, the technique is very promising for real-time detection and counting for RBC. In the latter (capillary segmentation) case, automatic simulation algorithm to generate the FF-OCT skin image data with embedded capillary networks in three-dimensional volume is presented. This algorithm simultaneously acquires augmented FF-OCT and ground truth images of capillary structures. Then, this dissertation performed deep learning with U-Net model using augmented datasets and later tested by real FF-OCT images cropped from in vivo human skin to assess the qualities of the synthesized image datasets. The result showed that the predicted segmentation can attend a certain accuracy level compared to the ground truth. This demonstrates that the proposed algorithm is capable to provide large dataset generation for labelled FF-OCT images of capillary network in human skin for research and deep learning, which established a basis for developing a more robust model to segment capillaries more accurately.

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