Objective

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).

Methods

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.

Results

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.

Conclusion

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.

中文翻译：

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SPECT心肌灌注成像的分类模型。

目的

这项工作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/或梗塞）。

方法

提出了两种不同的分类模型，即基于深度学习（DL）和基于知识的分类模型。第一类模型利用带有预训练的深度神经网络的转移学习和支持向量机分类器，该分类器具有从这些网络中提取的深浅特征。另一方面，后一种类型的模型旨在将专家读者的知识转换为适当的图像处理技术，包括特定的颜色阈值，分割，特征提取和一些启发式技术。此外，收集了来自192位患者（26-96岁，平均年龄61.5，男性38％，冠心病78％）的压力和休息图像的总和，以构成一个新的数据集。该数据集上两个专家阅读器的视觉评估被用作参考标准。

结果

对于基于DL的模型，最大准确度，灵敏度和特异性值分别计算为94％，88％和100％，对于基于知识的模型，最大准确度，灵敏度和特异性值分别计算为93％，100％和86％。

结论

提出的模型提供的诊断性能接近专家分析水平。因此，它们可以帮助临床决策解释有关心肌缺血和梗死的SPECT MPI。