Deep-Learning-Based Coronary Artery Calcium Detection from CT Image

被引:10
|
作者
Lee, Sungjin [1 ]
Rim, Beanbonyka [1 ]
Jou, Sung-Shick [2 ]
Gil, Hyo-Wook [2 ]
Jia, Xibin [3 ]
Lee, Ahyoung [4 ]
Hong, Min [5 ]
机构
[1] Soonchunhyang Univ, Dept Software Convergence, Asan 31538, South Korea
[2] Soonchunhyang Univ, Cheonan Hosp, Dept Internal Med, Cheonan 31151, South Korea
[3] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[4] Kennesaw State Univ, Dept Comp Sci, Kennesaw, GA 30144 USA
[5] Soonchunhyang Univ, Dept Comp Software Engn, Asan 31538, South Korea
基金
新加坡国家研究基金会;
关键词
calcium detection; coronary artery calcium score CT; resnet-50; VGG; inception resnet V2; deep learning; image classification; CLASSIFICATION; MODEL;
D O I
10.3390/s21217059
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Improved coronary artery calcium (CAC) detection in conventional CT with deep-learning image de-blurring
    Wuelker, Christian
    van der Werf, Niels R.
    Schnellbaecher, Nikolas D.
    Greuter, Marcel J. W.
    Grass, Michael
    MEDICAL IMAGING 2024: PHYSICS OF MEDICAL IMAGING, PT 1, 2024, 12925
  • [2] Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs
    Rim, Tyler Hyungtaek
    Lee, Chan Joo
    Tham, Yih-Chung
    Cheung, Ning
    Yu, Marco
    Lee, Geunyoung
    Kim, Youngnam
    Ting, Daniel S. W.
    Chong, Crystal Chun Yuen
    Choi, Yoon Seong
    Yoo, Tae Keun
    Ryu, Ik Hee
    Baik, Su Jung
    Kim, Young Ah
    Kim, Sung Kyu
    Lee, Sang-Hak
    Lee, Byoung Kwon
    Kang, Seok-Min
    Wong, Edmund Yick Mun
    Kim, Hyeon Chang
    Kim, Sung Soo
    Park, Sungha
    Cheng, Ching-Yu
    Wong, Tien Yin
    LANCET DIGITAL HEALTH, 2021, 3 (05): : E306 - E316
  • [3] Deep-Learning-Based Bughole Detection for Concrete Surface Image
    Yao, Gang
    Wei, Fujia
    Yang, Yang
    Sun, Yujia
    ADVANCES IN CIVIL ENGINEERING, 2019, 2019
  • [4] Deep-Learning-Based CT Motion Artifact Recognition in Coronary Arteries
    Elss, T.
    Nikisch, H.
    Wissel, T.
    Schmitt, H.
    Vembar, M.
    Morlock, M.
    Grass, M.
    MEDICAL IMAGING 2018: IMAGE PROCESSING, 2018, 10574
  • [5] Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography
    Nagayama, Yasunori
    Emoto, Takafumi
    Kato, Yuki
    Kidoh, Masafumi
    Oda, Seitaro
    Sakabe, Daisuke
    Funama, Yoshinori
    Nakaura, Takeshi
    Hayashi, Hidetaka
    Takada, Sentaro
    Uchimura, Ryutaro
    Hatemura, Masahiro
    Tsujita, Kenichi
    Hirai, Toshinori
    EUROPEAN RADIOLOGY, 2023, 33 (12) : 8488 - 8500
  • [6] Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography
    Yasunori Nagayama
    Takafumi Emoto
    Yuki Kato
    Masafumi Kidoh
    Seitaro Oda
    Daisuke Sakabe
    Yoshinori Funama
    Takeshi Nakaura
    Hidetaka Hayashi
    Sentaro Takada
    Ryutaro Uchimura
    Masahiro Hatemura
    Kenichi Tsujita
    Toshinori Hirai
    European Radiology, 2023, 33 : 8488 - 8500
  • [7] Feasibility of tongue image detection for coronary artery disease: based on deep learning
    Duan, Mengyao
    Mao, Boyan
    Li, Zijian
    Wang, Chuhao
    Hu, Zhixi
    Guan, Jing
    Li, Feng
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2024, 11
  • [8] Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images
    Wahab Sait, Abdul Rahaman
    Dutta, Ashit Kumar
    DIAGNOSTICS, 2023, 13 (07)
  • [9] Deep-Learning-Based Intravascular Ultrasound Segmentation for the Assessment of Coronary Artery Disease
    Nishi, Takeshi
    Yamashita, Rikiya
    Imura, Shinji
    Kozuka, Kazuki
    Yock, Paul G.
    Honda, Yasuhiro
    Fitzgerald, Peter
    CIRCULATION, 2020, 142
  • [10] Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey
    Pham, Nam Thanh
    Park, Chun-Su
    IEEE ACCESS, 2023, 11 : 11224 - 11237