Automatic Diagnosis of Coronary Artery Disease in SPECT Myocardial Perfusion Imaging Employing Deep Learning
Automatic Diagnosis of Coronary Artery Disease in SPECT Myocardial Perfusion Imaging Employing Deep Learning
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DOI：
10.3390/app11146362
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发表时间：
2021-07-01
影响因子：
2.7
通讯作者：
Papageorgiou, Elpiniki
中科院分区：
文献类型：
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作者：
Papandrianos, Nikolaos;Papageorgiou, Elpiniki
针对冠状动脉疾病（CAD）患者，本研究论文解决了使用单光子发射计算机断层扫描（SPECT）（Siemens Symbia S系列）心肌灌注成像（MPI）扫描自动诊断缺血或梗死的问题，并研究了深度学习和卷积神经网络的功能。考虑到深度学习在医学图像分类中的广泛适用性，引入了一种鲁棒的CNN模型，其架构先前在核图像分析中确定，通过提取图像的有洞察力的特征来识别心肌灌注图像，并使用它们来正确分类。此外，还实施了使用迁移学习的深度学习分类方法，以将SPECT MPI扫描的心血管图像分类为正常或异常（缺血或梗死）。目前的工作是区别于核心脏病学的其他研究，因为它利用SPECT MPI图像。为了解决CAD诊断的两类分类问题，实现足够的准确性，基于CNN探索过程构建了简单，快速和高效的CNN架构。然后，他们被用来确定CAD诊断的类别，展示其泛化能力。结果显示，所应用的方法足够准确，并且能够将梗死或缺血与健康患者区分开（总体分类准确度= 93.47% +/-2.81%，AUC评分= 0.936）。为了加强这项研究的结果，我们将提出的深度学习方法与其他流行的最先进的CNN架构进行了比较。预测结果显示了新的深度学习架构应用于使用SPECT MPI扫描的CAD诊断的有效性，超过了核医学中现有的深度学习架构。
Focusing on coronary artery disease (CAD) patients, this research paper addresses the problem of automatic diagnosis of ischemia or infarction using single-photon emission computed tomography (SPECT) (Siemens Symbia S Series) myocardial perfusion imaging (MPI) scans and investigates the capabilities of deep learning and convolutional neural networks. Considering the wide applicability of deep learning in medical image classification, a robust CNN model whose architecture was previously determined in nuclear image analysis is introduced to recognize myocardial perfusion images by extracting the insightful features of an image and use them to classify it correctly. In addition, a deep learning classification approach using transfer learning is implemented to classify cardiovascular images as normal or abnormal (ischemia or infarction) from SPECT MPI scans. The present work is differentiated from other studies in nuclear cardiology as it utilizes SPECT MPI images. To address the two-class classification problem of CAD diagnosis, achieving adequate accuracy, simple, fast and efficient CNN architectures were built based on a CNN exploration process. They were then employed to identify the category of CAD diagnosis, presenting its generalization capabilities. The results revealed that the applied methods are sufficiently accurate and able to differentiate the infarction or ischemia from healthy patients (overall classification accuracy = 93.47% +/- 2.81%, AUC score = 0.936). To strengthen the findings of this study, the proposed deep learning approaches were compared with other popular state-of-the-art CNN architectures for the specific dataset. The prediction results show the efficacy of new deep learning architecture applied for CAD diagnosis using SPECT MPI scans over the existing ones in nuclear medicine.