Deep Learning-based Epicardial Adipose Tissue Quantification System for Cardiovascular Disease Risk Analysis

  • 吳 昭儀

Student thesis: Doctoral Thesis


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
Date of Award2020
Original languageEnglish
SupervisorJung-Hsien Chiang (Supervisor)

Cite this