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

Abstract

Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In 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 conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions of interest and other regions using K-means clustering and the Grabcut algorithm. We 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 algorithm. All data used in this paper were collected at Soonchunhyang University Cheonan Hospital in Korea and the experimental test proceeded with IRB approval. The training was conducted using Resnet 50, VGG, and Inception resnet V2 models, and Resnet 50 had the best accuracy in validation and testing. Through the preprocessing process proposed in this paper, the accuracy of deep learning models was significantly improved by at least 10% and up to 40%.