A deep learning methodology for fully-automated quantification of calcific burden in high-resolution intravascular ultrasound images

The International Journal of Cardiovascular Imaging, vol. 42, pp. 87–98

Abstract

Quantification of the calcific burden is valuable in percutaneous coronary intervention (PCI) planning and in research to assess its changes after pharmacotherapies targeting plaque progression. In intravascular ultrasound (IVUS) images this analysis is currently performed manually and time consuming. To overcome these limitations, we introduce a deep-learning (DL) method for seamless detection of the calcific tissue. IVUS images from 197 vessels were analysed by an expert who identified the presence of calcium, and these estimations were used to train a DL model for fast detection of calcific deposits. The output of the model was tested in a set of 30 vessels against the estimations of the two experts. Comparison was performed at a frame-, lesion- and segment level. In total 26,211 frames were included in the training and 5,138 in the test set. The estimations of the DL method for the presence of calcium were similar to the experts (kappa 0.842 and 0.848, p < 0.001), while the correlation between the DL approach and the two experts for the arc of calcium (0.946 and 0.947, p < 0.001) and calcific area (0.745 and 0.706, p < 0.001) were high. Lesion- (0.971 and 0.990, p < 0.001) and segment-level analysis (0.980 and 0.981, p < 0.001) demonstrated a high correlation between the method and the two experts for calcific burden. The proposed DL method is able to accurately detect the calcific tissue and quantify its burden. These features render it useful in research and are expected to facilitate its application in the clinical workflows to guide PCI.

Authors 15

1. Xingwei He

   Barts Health NHS Trust · Tongji Hospital · Huazhong University of Science and Technology

   Affiliation as printed

   Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

   Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

2. Mohamed Mohamed

   Barts Health NHS Trust · University College London

   Affiliation as printed

   Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

   Institute of Health Informatics, University College London, London, UK

3. Nicole Ng

   Barts Health NHS Trust

   Affiliation as printed

   Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

4. Thamil Kumaran S M

   Queen Mary University of London · William Harvey Research Institute

   Affiliation as printed

   Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK

5. Retesh Bajaj

   Queen Mary University of London · Barts Health NHS Trust · William Harvey Research Institute

   Affiliation as printed

   Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK

   Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

6. Nathan Angelo Lecaros Yap

   Barts Health NHS Trust

   Affiliation as printed

   Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

7. Emrah Erdoğan

   Van Yüzüncü Yıl Üniversitesi

   Affiliation as printed

   Department of Cardiology, Faculty of Medicine, Yuzuncu Yil University, Van, Turkey

8. Gönül Zeren

   Queen Mary University of London · William Harvey Research Institute

   Affiliation as printed

   Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK

9. Anthony Mathur

   Queen Mary University of London · Barts Health NHS Trust · William Harvey Research Institute

   Affiliation as printed

   Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK

   Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

10. Ahmet Emir Ulutaş

    Queen Mary University of London · William Harvey Research Institute

    Affiliation as printed

    Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK

11. Bo Gao

    Suizhou Central Hospital

    Affiliation as printed

    Department of Cardiology, Affiliated Hospital of Hubei, Suizhou Central Hospital, University of Medicine, Suizhou, China

12. Yao‐Jun Zhang

    Xuzhou Cancer Hospital

    Affiliation as printed

    Department of Cardiology, Xuzhou Third People's Hospital, Xuzhou, China

13. Andreas Baumbach

    Queen Mary University of London · Barts Health NHS Trust · William Harvey Research Institute

    Affiliation as printed

    Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK

    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK

14. Jouke Dijkstra Aachen Division of Image Processing Department of Radiology, Leiden University Medical Center

    Leiden University · Leiden University Medical Center

    Affiliation as printed

    Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

15. Christos V. Bourantas corresponding

    National Health Service · Queen Mary University of London · Barts Health NHS Trust · William Harvey Research Institute

    Affiliation as printed

    Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University, London, UK. cbourantas@gmail.com

    Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK. cbourantas@gmail.com