Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
| العنوان: | Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis |
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| المؤلفون: | Robbert W. van Hamersvelt, Michiel Voskuil, Tim Leiner, Nikolas Lessmann, Majd Zreik, Ivana Išgum, Jelmer M. Wolterink, Max A. Viergever |
| المصدر: | Medical Image Analysis, 44, 72. Elsevier |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Male, FOS: Computer and information sciences, Computed Tomography Angiography, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Contrast Media, Fractional flow reserve, 030204 cardiovascular system & hematology, Coronary Angiography, 030218 nuclear medicine & medical imaging, 0302 clinical medicine, Medicine, Functionally significant coronary artery stenosis, Radiological and Ultrasound Technology, Middle Aged, Computer Graphics and Computer-Aided Design, medicine.anatomical_structure, Radiology Nuclear Medicine and imaging, Cardiology, Convolutional autoencoder, Female, Computer Vision and Pattern Recognition, Algorithms, Artery, medicine.medical_specialty, Heart Ventricles, Iohexol, Cardiac-Gated Imaging Techniques, Convolutional neural network, Health Informatics, Sensitivity and Specificity, 03 medical and health sciences, Sørensen–Dice coefficient, Internal medicine, Humans, Radiology, Nuclear Medicine and imaging, Coronary CT angiography, Receiver operating characteristic, business.industry, Deep learning, Coronary Stenosis, Reproducibility of Results, medicine.disease, Coronary arteries, Stenosis, Ventricle, Artificial intelligence, business |
| الوصف: | In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA). The study includes consecutively acquired CCTA scans of 166 patients with FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted and clustered encodings. Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 +- 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively. The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This paper was submitted in April 2017 and accepted in November 2017 for publication in Medical Image Analysis. Please cite as: Zreik et al., Medical Image Analysis, 2018, vol. 44, pp. 72-85 |
| وصف الملف: | image/pdf |
| اللغة: | English |
| تدمد: | 1361-8415 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c60eb72839bbbcddc6b81a3904ee4e13 https://hdl.handle.net/1874/364221 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....c60eb72839bbbcddc6b81a3904ee4e13 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13618415 |
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