系統識別號 U0026-1208202018041300
論文名稱(中文) 透過深度學習自動量化心內膜脂肪以輔助分析心血管疾病之風險
論文名稱(英文) Deep Learning-based Epicardial Adipose Tissue Quantification System for Cardiovascular Disease Risk Analysis
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 吳昭儀
研究生(英文) Chao-Yi Wu
學號 P76074672
學位類別 碩士
語文別 英文
論文頁數 52頁
口試委員 指導教授-蔣榮先
中文關鍵字 心內膜脂肪  影像分割  心血管疾病  機器學習  預防醫學  醫療影像 
英文關鍵字 Epicardial Adipose Tissue  image segmentation  machine learning  deep learning  risk analysis  preventive health 
中文摘要 心血管疾病是全球最常見的死亡原因之一,其危險因子如是否罹患糖尿病、抽菸、肥胖等,常被使用於評估罹患風險,因此頗受預防性醫學所重視。近年來逐漸有研究發現心內膜脂肪與心血管疾病有密切關係,主要透過活體組織切片實際分析該脂肪,少部分如鈣化指數在醫療影像中進行觀察可得,可用於分析血管狹窄程度。鑒於近年來深度學習在醫療影像的成功,本研究將以CT影像為輸入資料,採取深度學習模型自動擷取並量化心內膜脂肪,並進一步探討與心血管疾病之關聯性,驗證心內膜脂肪組織是否可成為危險因子之一,以輔助醫師進行心血管疾病的風險分析。
我們基於U-Net架構建立模型從CT影像中擷取心內膜脂肪,其中採取ResNeXt模組進行特徵捲積,並在skip connection上加入scSE機制,將encoder的資訊以兩種注意力機制加強後融合一起,再以後處理方式將心內膜脂肪量化成體積,同時為減少個體之間身形差異的因素,使用BSA對心內膜脂肪體積進行校正。在實驗中,本研究收集了成大醫院4954位病人之CT影像資料與11種臨床病史,在分割心內膜脂肪任務上,我們分別嘗試了弱監督式學習與監督式學習等10種以上的模型,最終選用基於ResNeXt加上scSE 機制的模型,並在DSC達到0.89。
英文摘要 Cardiovascular disease is the most common cause of death worldwide, always ranked in the top 3 of the leading causes of death in Taiwan. The importance of risk factors for cardiovascular disease has been attached great significance. Recently, studies focused on fat around arterial vessels inside the pericardium, the epicardial adipose tissue. In our research, we associate epicardial adipose tissue with cardiovascular diseases to find evidence that may support epicardial adipose tissue as a new risk factor.
To obtain epicardial adipose tissue rapidly, we design a system aim to automatically extract the fat tissue and provide the quantified information to doctors. Inspired by the success of deep learning model on image tasks, we build our segmentation model based on U-net and did some data augmentation. With scSE mechanism applied on skip connection and SE-RestNeXt block as the backbone. Our system achieved over 0.89 Dice similarity coefficient on the test data. Depends on the data augmentation, we can perform well on enlarged CT scan images and non-contrast CT scan images.
We conduct our experiment in contrasted CT scan image data from NCKUH 4954 patients. There are 11 types of diseases record and several basic personal information included in the data. To eliminate the inherent difference of each shape, we firstly revise the fat volume with the body surface area. We analyze the relation between epicardial adipose tissue volume and diseases with p-value. We found that diabetes, hyperlipidemia, hypertension, chronic kidney disease, stroke, arrhythmia, acute myocardial infarction, heart failure, peripheral artery occlusive disease, and coronary artery disease are significantly different. As for coronary artery disease with zero calcium score, the odds ratio of EAT volume shows 1.004 with p-value<0.5, leading more EAT volume may cause higher risk. Thus, providing EAT volume can improve preventive health and help doctors diagnosis when the calcium score equals 0.
論文目次 中文摘要 I
Abstract III
誌謝 V
Contents VI
List of Figures VIII
List of Tables XI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Research Objectives 4
1.4 Thesis Organization 5
Chapter 2 Related Work 6
2.1 Risk Factors of Cardiovascular Disease 6
2.2 Epicardial Adipose Tissue in CT scan 7
2.3 Deep Learning in Medical Image 8
Chapter 3 Epicardial Adipose Tissue Segmentation 10
3.1 Preliminary Study 10
3.1.1 Weakly-supervised Learning 11
3.1.2 Supervised Learning 13
3.2 EAT Segmentation Model 17
3.2.1 One-stage Strategy 17
3.2.2 Two-stage Strategy 18
3.3 2D pixel to 3D volume 21
3.4 Body Surface Area Revision 22
Chapter 4 Experiments 23
4.1 Experimental Design 23
4.2 Data Description 24
4.3 Evaluation 26
4.4 EAT Extraction 27
4.4.1 Data Preprocess 27
4.4.2 EAT Segmentation 29
4.5 Case Study 32
4.6 Different Thickness and Scale Effect 35
4.7 Relation between BSA modified EAT Volume and Disease 39
Chapter 5 Conclusions and Future Works 46
5.1 Conclusions 46
5.2 Future Works 47
Reference 48
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