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系統識別號 U0026-2508202016052600
論文名稱(中文) 利用動態顯影磁振造影及機器學習方法對前縱膈腔腫瘤進行分類
論文名稱(英文) Classification of Anterior Mediastinal Tumors with Dynamic Contrast Enhanced Magnetic Resonance Imaging and Machine Learning Methods
校院名稱 成功大學
系所名稱(中) 生物醫學工程學系
系所名稱(英) Department of BioMedical Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 唐士堯
研究生(英文) Shih-Yao Tang
學號 P86071090
學位類別 碩士
語文別 英文
論文頁數 56頁
口試委員 指導教授-陳家進
口試委員-林哲偉
口試委員-蔡育秀
口試委員-林佳瀅
口試委員-吳馬丁
中文關鍵字 DCE-MRI  anterior mediastinal tumor  compartment model  machine learning 
英文關鍵字 DCE-MRI  anterior mediastinal tumor  compartment model  machine learning 
學科別分類
中文摘要 在醫學影像中,前縱膈腔的腫瘤看起來非常相似,在影像上目前還沒有一個標準流程去判斷胸腺瘤或是淋巴瘤,只能憑藉著醫生的經驗。我們的目標是找到能定量分類前縱膈腔腫瘤的參數或是方法,來輔助醫生在臨床上診斷。在這份研究中,我們利用動態顯影磁振造影(Dynamic Contrast Enhanced Magnetic Resonance Imaging, DCE-MRI)這項醫學影像技術,並利用不同的生理模型去分析前縱膈腔腫瘤的血流灌注參數,再對這些參數使用機器學習的方法分類,來找出能夠分類前縱膈腔腫瘤的參數與分類方式,以建立處理動態顯影磁振造影的標準方法。 方法:本研究蒐集了50位來自成功大學附設醫院的病人DICOM資料;其中13位患有淋巴癌;37位患有胸腺瘤。我們自行開發程式圈選感興趣的區域(Volume of Interest, VOI)以及計算動脈輸入函數(Arterial Input Function, AIF),並將其影像強度轉換成顯影劑在組織內的濃度。我們共使用三種不同的生理模型:Tofts Kermode(TK)、Extended Tofts Kermode(ETK)和Two-compartment exchange(2CXM)模型去分析灌注參數。再將灌注參數利用接受者操作特徵曲線(Receiver operating characteristic, ROC)以及三種不同的機器學習方法;決策樹(Decision tree)、支撐向量機(Support vector machine, SVM)和K近鄰演算法(K-nearest neighbor, KNN)進行分類,並觀察三種生理模型的滲透參數差異,以及比較不同的機器學習演算法的分類結果。 結果:我們利用了自己的程式透明化動態顯影磁振造影數據分析流程,並使用機器學習模型再分類胸腺瘤以及淋巴癌上,取得了80%的正確率。並且在分類胸腺瘤以及胸腺癌上取得正確率、特異度和敏感度三項都超過92%的判斷效果。 結論:可使用兩隔室模型分析動態顯影磁振造影數據,計算出灌注參數。利用灌注參數訓練而成的決策樹分類模型去分類前縱膈腔腫瘤,並再未來加上更多的前縱膈腔腫瘤資料加以優化模型,在臨床診斷時可提供可靠的建議。
英文摘要 In current clinical and imaging exams of anterior mediastinal tumors, one existing challenge exists in the differentiation of thymic tumors and lymphoma. Our aim is to
quantitatively classify anterior mediastinal tumor with image-derived parameters and classification models to assist doctors in clinical diagnosis. In this study, we used the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) as the main imaging modality. Moreover, we used three different compartment models to analyze the perfusion parameters from DCE-MRI data to evaluate the anterior mediastinal tumors. Method: In this study, we collected data from fifty patients, including imaging and clinical history, from the National Cheng Kung University Hospital. Biopsy or surgical pathology found 13 of them with lymphoma and 37 of them with the thymic tumor. We drew the volume of interest (VOI) and the arterial input function (AIF) with our in-house software, and converted the image intensity into the concentration of the contrast agent (CA) in the tissue. Moreover, we use three different compartment models, Tofts Kermode (TK) model, Extended Tofts Kermode (ETK) model, and two-compartment exchange (2CXM) model to calculate the perfusion parameters. We used the receiver operating characteristic (ROC) curve and three different machine learning methods including decision trees, support vector machine (SVM), and K-nearest neighbor (KNN). Finally, we evaluated the differences in the perfusion parameters of three compartment models and compared the classification results of different machine learning algorithms. Result: We make the process of DCE-MRI data analysis transparent with our in-house software. Moreover, we classified lymphomas and thymic tumors by our decision tree models and eventually reached 80% on accuracy. Furthermore, the accuracy, sensitivity, and specificity of the result of classifying thymomas and thymic carcinomas can all surpass 92%. Conclusion: Our software can analyze the DCE-MRI data with the two-compartments model and calculate the perfusion parameters. We used the perfusion parameters to train the decision tree classification model which can classify the anterior mediastinal tumors. We will add more tumor data in the future to optimize the classification model provide a reliable computer-assisted diagnosis in the clinical setting.
論文目次 Chapter1 Introduction 1
1.1 Cancer: the top health threat in Taiwan 1
1.2 Mediastinal tumors 2
1.2.1 Mediastinum 2
1.2.2 Tumors in the anterior mediastinum 4
1.2.3 Treatment and prognosis of mediastinal tumor 5
1.3 Magnetic Resonance Imaging (MRI) 6
1.3.1 Principle of the MRI 6
1.3.2 Clinical use of the MRI 7
Chapter2 Materials and Methods 8
2.1 Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data analysis 8
2.1.1 Dynamic contrast enhanced magnetic resonance imaging 8
2.1.2 Data Acquisition 10
2.1.3 Volume of interest drawing 10
2.1.4 Arterial Input Function 12
2.1.6 Compartment model 14
2.1.7 Tofts Kermode model (TK model) 15
2.1.8 Extended Tofts Kermode model (ETK Model) 16
2.1.9 Two-compartment exchange model (2CXM model) 17
2.1.10 Nonlinear Least Squares Curve Fitting 19
2.2 Classification 20
2.2.2 Decision tree 21
2.2.3 Support vector machine (SVM) 23
2.2.4 K-nearest neighbor (KNN) 24
Chapter 3 Results and Discussion 25
3.1 Concentration-time Curve 25
3.1.1 The result of AIF and VOI 25
3.1.2 The result of two-compartment models 27
3.2 The results of classification 30
3.2.1 Raw data result of parameters 30
3.2.2 The labels of classification 31
3.2.3 Receiver operating characteristic curve (ROC) 34
3.2.4 The multi-parameter classifiers trained by all 20 features 40
3.2.5 The model trained by 2 of 20 parameters 42
3.2.6 Important parameters of decision tree 45
3.3 Limitation and future work 53
Chapter 4 Conclusion 54
Reference 55

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