In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.

中文翻译：

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使用深度学习模型和手工特征在 IVUS 图像中自动检测流明边界

在血管内超声（IVUS）图像的临床分析中，管腔大小是冠状动脉粥样硬化的重要指标，也是冠状动脉疾病诊断和介入治疗的前提。在这项研究中，提出了一种基于深度学习模型和手工特征的全自动方法来检测 IVUS 图像中的管腔边界。首先，从 IVUS 图像中提取了 193 个手工制作的特征。然后将手工特征与从 U-Net 中提取的 64 个高级特征相结合，构建混合特征向量。为了获得贡献较大的特征子集，我们采用扩展的二进制布谷鸟搜索进行特征选择。最后，选择的36维混合特征子集用于使用基于核稀疏编码的字典学习对测试图像进​​行分类。所提出的算法在公开可用的数据集上进行测试，并使用三个指标进行评估。通过消融实验，实验结果的平均值（Jaccard：0.88，Hausdorff 距离：0.36，面积差的百分比：0.06）证明可以有效地提高管腔边界检测。此外，与最近在同一数据集上使用的方法相比，所提出的方法表现出良好的性能和高精度。06) 被证明可以有效改善流明边界检测。此外，与最近在同一数据集上使用的方法相比，所提出的方法表现出良好的性能和高精度。06) 被证明可以有效改善流明边界检测。此外，与最近在同一数据集上使用的方法相比，所提出的方法表现出良好的性能和高精度。