Kaplan Berkaya Selcan, Ak Sivrikoz Ilknur, Gunal Serkan
Department of Computer Engineering, Faculty of Engineering, Eskisehir Technical University, Eskisehir, Turkiye.
Department of Nuclear Medicine, Faculty of Medicine, Eskisehir Osmangazi University, Eskisehir, Turkiye.
Comput Biol Med. 2020 Aug;123:103893. doi: 10.1016/j.compbiomed.2020.103893. Epub 2020 Jul 15.
The main goal of this work is to develop computer-aided classification models for single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) to identify perfusion abnormalities (myocardial ischemia and/or infarction).
Two different classification models, namely, deep learning (DL)-based and knowledge-based, are proposed. The first type of model utilizes transfer learning with pre-trained deep neural networks and a support vector machine classifier with deep and shallow features extracted from those networks. The latter type of model, on the other hand, aims to transform the knowledge of expert readers to appropriate image processing techniques including particular color thresholding, segmentation, feature extraction, and some heuristics. In addition, the summed stress and rest images from 192 patients (age 26-96, average age 61.5, 38% men, and 78% coronary artery disease) were collected to constitute a new dataset. The visual assessment of two expert readers on this dataset is used as a reference standard. The performances of the proposed models were then evaluated according to this standard.
The maximum accuracy, sensitivity, and specificity values are computed as 94%, 88%, and 100% for the DL-based model and 93%, 100%, and 86% for the knowledge-based model, respectively.
The proposed models provided diagnostic performance close to the level of expert analysis. Therefore, they can aid in clinical decision making for the interpretation of SPECT MPI regarding myocardial ischemia and infarction.
本研究的主要目标是开发用于单光子发射计算机断层扫描（SPECT）心肌灌注成像（MPI）的计算机辅助分类模型，以识别灌注异常（心肌缺血和/或梗死）。
提出了两种不同的分类模型，即基于深度学习（DL）的模型和基于知识的模型。第一种模型利用预训练深度神经网络的迁移学习以及从这些网络中提取深度和浅层特征的支持向量机分类器。另一方面，后一种模型旨在将专家读者的知识转化为适当的图像处理技术，包括特定的颜色阈值处理、分割、特征提取和一些启发式方法。此外，收集了192例患者（年龄26 - 96岁，平均年龄61.5岁，男性占38%，冠心病患者占78%）的负荷和静息图像总和，以构成一个新的数据集。两位专家读者对该数据集的视觉评估用作参考标准。然后根据该标准评估所提出模型的性能。
基于深度学习的模型的最大准确率、灵敏度和特异性值分别计算为94%、88%和100%，基于知识的模型的相应值分别为93%、100%和86%。
所提出的模型提供的诊断性能接近专家分析水平。因此，它们有助于在SPECT MPI心肌缺血和梗死解读方面的临床决策。
