Cardiac CT Image Segmentation for Deep Learning-Based Coronary Calcium Detection Using K-Means Clustering and Grabcut Algorithm

作     者：Sungjin Lee Ahyoung Lee Min Hong 

作者机构：Department of Software ConvergenceSoonchunhyang UniversityAsan31538Korea Department of Computer ScienceKennesaw State UniversityMarietta30144GAUSA Department of Computer Software EngineeringSoonchunhyang UniversityAsan31538Korea 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：2543-2554页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was supported under the framework of an international cooperation program managed by the National Research Foundation of Korea(NRF-2019K1A3A1A20093097) supported by the National Key Research and Development Program of China(2019YFE0107800) was supported by the Soonchunhyang University Research Fund 

主　　题：Deep learning VGG resnet CT image processing 

摘      要：Specific medical data has limitations in that there are not many numbers and it is not *** solve these limitations,it is necessary to study how to efficiently process these limited amounts of *** this paper,deep learning methods for automatically determining cardiovascular diseases are described,and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was *** cardiac CT images include several parts of the body such as the heart,lungs,spine,and *** preprocessing step proposed in this paper divided CT image data into regions of interest and other regions using K-means clustering and the Grabcut *** compared the deep learning performance results of original data,data using only K-means clustering,and data using both K-means clustering and the Grabcut *** data used in this paper were collected at Soonchunhyang University Cheonan Hospital in Korea and the experimental test proceeded with IRB *** training was conducted using Resnet 50,VGG,and Inception resnet V2 models,and Resnet 50 had the best accuracy in validation and *** the preprocessing process proposed in this paper,the accuracy of deep learning models was significantly improved by at least 10%and up to 40%.