BACKGROUND IVUS is widely used to quantitatively assess coronary artery disease. The purpose of this study was to automatically characterize dense calcium (DC) tissue in the gray scale intravascular ultrasound (IVUS) images using the image textural features. METHODS A total of 316 Gy-scale IVUS and corresponding virtual histology images from 26 patients with acute coronary syndrome who underwent IVUS along with X-ray angiography between October 2009 to September 2014 were retrospectively acquired and analyzed. One expert performed all procedures and assessed their IVUS scans. After image acquisition, the DC candidate and corresponding acoustic shadow regions were automatically determined. Then, nine image-base feature groups were extracted from the DC candidates. In order to reduce the dimensionalities, principal component analysis (PCA) was performed, and selected feature sets were utilized as an input for a deep belief network. Classification results were validated using 10-fold cross validation. RESULTS The dimensionality of the feature map was efficiently reduced by 50% (from 66 to 33) without any performance decrease using PCA method. Sensitivity, specificity, and accuracy of the proposed method were 92.8 ± 0.1%, 85.1 ± 0.1%, and 88.4 ± 0.1%, respectively (p < 0.05). We found that the window size could largely influence the characterization results, and selected the 5 × 5 size as the best condition. We also validated the performance superiority of the proposed method with traditional classification methods. CONCLUSIONS These experimental results suggest that the proposed method has significant clinical applicability for IVUS-based cardiovascular diagnosis.

中文翻译：

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使用深度置信网络对灰度血管内超声图像中的密集钙组织进行自动分类。

背景技术IVUS被广泛用于定量评估冠状动脉疾病。这项研究的目的是使用图像纹理特征自动表征灰度血管内超声（IVUS）图像中的致密钙（DC）组织。方法回顾性分析2009年10月至2014年9月行IVUS并接受X线血管造影的26例急性冠脉综合征患者的316个Gy尺度IVUS和相应的虚拟组织学图像。一位专家执行了所有程序并评估了他们的IVUS扫描。图像采集后，将自动确定DC候选对象和相应的声影区域。然后，从DC候选项中提取了九个基于图像的特征组。为了减少尺寸，进行主成分分析（PCA），并将选定的特征集用作深度信念网络的输入。使用10倍交叉验证对分类结果进行验证。结果特征图的维数有效地降低了50％（从66降低到33），而使用PCA方法却没有任何性能下降。该方法的灵敏度，特异性和准确性分别为92.8±0.1％，85.1±0.1％和88.4±0.1％（p <0.05）。我们发现，窗口大小可能在很大程度上影响表征结果，并选择5×5大小作为最佳条件。我们还用传统的分类方法验证了该方法的性能优越性。