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A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation

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Abstract

Myocardial ischemia diagnosis with CT perfusion imaging (CTP) is important in coronary artery disease management. Traditional analysis procedure is time-consuming and error-prone due to the semi-manual and operator-dependent nature. To improve the diagnostic performance, a deep learning-based, fully automatic, and clinical-ready framework was developed. Two collaborating deep learning networks including a 3D U-Net for left ventricle segmentation and a CNN for anatomical landmarks detection were trained on 276 subjects. With our processing framework, the 17-segment left ventricular model was automatically generated conformed to the clinical standard. Myocardial blood flow computed by commercial software was extracted within each segment and visualized against the bull’s eye plot. The performance was validated on another 45 subjects. Coronary angiography and invasive fractional flow reserve measurements were also performed in these patients to serve as the gold standard for myocardial ischemia diagnosis. As a result, the diagnostic accuracy for our method was 81.08%, much higher than that for commercially available CTP analysis software (56.75%), and our method demonstrated a higher consistency (Kappa coefficient 0.759 vs. 0.585). Besides, the average processing time of our method was much lower (30 ± 10.5 s/subject vs. over 30 min/subject). In conclusion, the proposed deep learning-based framework could be a promising tool for assisting CTP analysis.

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Funding

This study was supported by the Shenzhen Science and Technology Program (grant number KQTD2016112809330877), the Clinical Research Center of Shandong University (grant number 2020SDUCRCB005), the Key Research and Development Program of Shandong Province (grant number 2020ZLYS05), and the Shenyang Science and Technology Plan Project (grant number 20–205-4–014).

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Author notes

  1. Co-first authors Mujun An and Junhuan Li contributed equally to this work.

Authors and Affiliations

  1. The Research Center of Intelligent Medical Information Processing, School of Information Science and Engineering, Shandong University, Qingdao, 266237, China

    Mujun An & Zhi Liu

  2. Shenzhen Keya Medical Technology Corporation, 2B-604 Tianan Cyber Park, Longcheng Street, Shenzhen, 518000, China

    Mujun An, Junhuan Li, Xiaoyang Xu, Kunlin Cao, Qi Song & Yuwei Li

  3. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science and Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA

    U. Joseph Schoepf & Rock H. Savage

  4. Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education, Chinese National Health Commission and Chinese Academy of Medical Science, The State and Shandong Province Joint Key Laboratory of Translational Medicine, Jinan, 250012, China

    Zeying Wang & Pengfei Zhang

  5. Department of Cardiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, No.107 Wenhuaxi Road, Jinan, 250012, Shandong, China

    Pengfei Zhang

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  1. Mujun An
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Corresponding authors

Correspondence to Yuwei Li or Pengfei Zhang.

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Conflict of interest

Dr. Schoepf receives institutional research support and/or personal fees for speaking and consulting from Bayer, Bracco, GE, Guerbet, HeartFlow Inc., Keya Medical, and Siemens Healthineers. MA, JL, XX, RHS, KC, QS, YL, PZ, and ZL have no potential competing interests to declare.

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An, M., Li, J., Xu, X. et al. A deep learning-based fully automatic and clinical-ready framework for regional myocardial segmentation and myocardial ischemia evaluation. Med Biol Eng Comput 61, 1507–1520 (2023). https://doi.org/10.1007/s11517-023-02798-y

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  • DOI: https://doi.org/10.1007/s11517-023-02798-y

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