利用機器學習方法對肝纖維化進行早期檢測和分類:基於影像的生物標記和數據驅動的計算技術在動態對比增強磁共振成像

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蘇逸鎮(Yi-Zhen Su) 查詢紙本館藏

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論文名稱

利用機器學習方法對肝纖維化進行早期檢測和分類:基於影像的生物標記和數據驅動的計算技術在動態對比增強磁共振成像
(Early Detection and Classification of Liver Fibrosis with Machine Learning Methods: Image-Based Biomarkers and Data-driven Computational Techniques in Dynamic Contrast-Enhanced MRI)

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至系統瀏覽論文 (2026-1-31以後開放)

摘要(中)

近期，慢性肝病已成為對健康產生嚴重影響的疾病。在肝纖維化的早期，有效的治療和適當的飲食管理可能有助於康復。然而，目前常用的無侵入性診斷方法，如血液檢查和腹部超聲，對於肝纖維化的早期階段檢測並不十分靈敏。因此，在這項研究中，我們提出了使用機器學習(ML)技術，透過特徵提取的方式，以預測肝纖維化階段的方法。此研究結果有兩個部分，分別為動物的小鼠實驗與人類臨床數據。
小鼠實驗部分，分為健康(A0 )、輕度(A1 )、中度(A2 )或重度(A3 )四類。具體來說，我們採用支持向量機作為分類器。使用兩種類型的特徵，即來自小鼠磁共振 (MR)成像的歐拉數(EN)與肝纖維化相關的估計孔隙率，用於訓練分類器。這兩種特徵提取的基本想法源自於肝臟成像的幾何和拓撲特性。在參數調整之後，最終，在
模型比較方面，使用兩種特徵分別為 binary(Acc=63.2%)和 grayscale(Acc=64.5%) 影像給 CNN 模型訓練，和在 SVM 模型上，不同特徵作為訓練，分別為單獨的估計孔隙率(Acc=93.3%)、單獨的歐拉特徵數(Acc=74.6%)和全部特徵(Acc=90.9%)。使用單獨的估計孔隙率特徵的模型在四類別的綜合表現上優於其他模型，特別是在輕度(A1 )類別中表現卓越。
進一步地，我們轉向人類肝纖維化分類，使用了 62 位病人的臨床數據，包括血液檢查和 DCE-MRI 訊號曲線。為了增加準確性，我們引入了模擬肝臟的新特徵，如孔隙率、擴散率、肝門靜脈和肝動脈的流速。通過結合這些特徵，我們使用 KNN 和 Naive Bayes 模型在 F0-3 vs F4-6 和 F0-5 vs F6 分類中取得了優異的結果，綜合三類精確度保持在 69.4%。這項研究強調了模擬肝臟訊號濃度模型在肝纖維化評估中的潛在價值，同時提供了生物醫學研究者更深入的理解和新的研究方向。

摘要(英)

In recent years, chronic liver disease has emerged as a condition significantly impacting health. Effective treatment and proper dietary management in the early stages of liver fibrosis may contribute to recovery. However, non-invasive diagnostic methods such as blood tests and abdominal ultrasound are not highly sensitive for detecting early stages of liver fibrosis. Therefore, in this study, we propose using machine learning (ML) techniques, employing feature extraction, to predict the stages of liver fibrosis. The study comprises experiments on mice and clinical data from human subjects.
In the mouse experiment section, mice were categorized into healthy (A0), mild (A1), moderate (A2), or severe (A3) stages. Specifically, we utilized support vector machines as classifiers, using two types of features: Euler numbers (EN) from mouse magnetic resonance (MR) imaging and estimated porosity related to liver fibrosis. The basic idea behind these feature extractions stems from the geometric and topological properties of liver imaging. After parameter tuning, the final model comparisons showed that using the two features separately for binary (Acc=63.2%) and grayscale (Acc=64.5%) images for training CNN models, as well as on SVM models using different features—solely esti- mated porosity (Acc=93.3%), solely Euler characteristic numbers (Acc=74.6%), and all features combined (Acc=90.9%). The model using solely estimated porosity as a feature outperformed other models’ overall performance across the four categories, particularly excelling in the mild (A1) category.
Furthermore, we turned to human liver fibrosis classification, utilizing clinical data from 62 patients, including blood tests and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) signal curves. To enhance accuracy, we introduced new features simulating liver properties, such as porosity, diffusion rate, and flow speeds of the portal vein and hepatic artery. Combining these features, we used KNN and Naive Bayes models to achieve excellent results in F0-3 vs. F4-6 and F0-5 vs. F6 classifications, with an overall three-class accuracy of 69.4%. This study underscores the potential value of simulating liver signal concentration models in liver fibrosis assessment, providing biomedical researchers with a deeper understanding and new avenues for research.

關鍵字(中)

★ 肝纖維化
★ 磁振造影
★ 動態對比增強磁振造影
★ 多孔介質
★ 達西方程式
★ 對流擴散方程式
★ 最佳化演算法

關鍵字(英)

★ liver fibrosis
★ MRI
★ DCE-MRI
★ porous medium
★ Darcy equation
★ convection-diffusion equation
★ Optimization Algorithm

論文目次

致謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
表目錄........................................... xi
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1緒論.......................................... 1
1.1 肝臟的功能與慢性肝病............................. 1
1.2 臨床慢性肝病檢測 ............................... 2
1.3 肝纖維化分期.................................. 3
1.4 磁共振成像技術原理.............................. 4
1.5 動態對比增強磁振造影............................. 5
1.6 研究動機與目的................................. 7
1.7 本論文章節架構計畫.............................. 8
2 數據分析方法..................................... 9
2.1 統計分析工具.................................. 9
2.1.1 敘述統計和卡方檢定.......................... 10
2.1.2 變異數分析和Kruskal-WallisH檢定................. 10
2.1.3 Logistic迴歸 .............................. 11
2.2 機器學習 .................................... 13
2.2.1 分類方法:支持向量機(Support Vector Machine，SVM) . . . . 14
2.2.2 特徵選取(featureSelection) .................... 17
2.3 深度學習:卷積神經網路(Convolutional Neural Network, CNN) . . . . 18
2.3.1 卷積層.................................. 19
viii
2.3.2 池化層.................................. 20
2.3.3 全連接層 ................................ 22
2.3.4 超參數(hyperparameter) ...................... 22
2.3.5 深度學習可視化算法.......................... 28
2.4 資料增廣 .................................... 29
2.5 衡量指標 .................................... 31
2.5.1 混淆矩陣 ................................ 31
2.5.2 ROC曲線................................ 32
2.6 最佳化演算法.................................. 33
3 文獻回顧:人工智慧在醫學相關領域的應用 .................... 36
3.1 人工智慧在肝病診斷技術 ........................... 37
3.2 人工智慧在肝纖維化分期診斷技術 ...................... 37
3.2.1 臨床肝纖維化分期 ........................... 37
3.2.2 動物肝纖維化研究 ........................... 39
4 文獻回顧:肝臟數值模擬 .............................. 41
4.1 肝臟微觀模擬.................................. 41
4.2 肝臟巨觀模擬.................................. 42
5 動物實驗樣本..................................... 43
5.1 小鼠肝纖維化模型和MRI量測........................ 43
5.2 組織學程序................................... 43
5.3 小鼠肝纖維化組織學評估 ........................... 44
5.4 肝纖維化評估結果與MRI影像分析 ..................... 44
6 數值小鼠模型實驗設計................................ 47
6.1 預處理流程................................... 47
6.1.1 肝臟分割的圖像預處理 ........................ 48
6.1.2 資料增廣 ................................ 48
6.1.3 特徵提取 ................................ 49
6.2 CNN模型訓練流程............................... 52
6.3 SVM模型訓練流程............................... 53
7 結果與討論 ...................................... 55
7.1 統計分析 .................................... 55
7.2 CNN結果.................................... 56
7.2.1 binaryandgrayscale的結果...................... 62
7.3 SVM結果.................................... 62
7.4 我們的方法與其他研究結果的比較 ...................... 67
8 基於動態對比增強 MRI 的數據驅動計算技術，用於早期檢測慢性肝病 . . . . 70
8.1 達西方程式................................... 71
8.2 隨時間變化的對流擴散方程式......................... 73
8.2.1 求解演算法............................... 74
8.3 MATLAB程式碼驗證 ............................. 76
8.4 肝內部訊號模擬演算法............................. 83
8.5 病人資料 .................................... 87
8.6 特徵提取 .................................... 87
9 結論與未來相關研究建議 .............................. 97
參考文獻 .........................................100
1 附錄 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

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指導教授

黃楓南(Feng-Nan Hwang)

審核日期

2024-1-24

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