Lee Jimmy Addison, Wong Damon Wing Kee, Taruya Akira, Tanaka Atsushi, Foin Nicolas, Wong Philip
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1501-1504. doi: 10.1109/EMBC.2017.8037120.
Identifying vulnerable plaque is important in coronary heart disease diagnosis. Recent emerged imaging modality, Intravascular Optical Coherence Tomography (IVOCT), has been proved to be able to characterize the appearance of vulnerable plaques. Comparing with the manual method, automated fibroatheroma identification would be more efficient and objective. Deep convolutional neural networks have been adopted in many medical image analysis tasks. In this paper, we introduce deep features to resolve fibroatheroma identification problem. Deep features which extracted using four deep convolutional neural networks, AlexNet, GoogLeNet, VGG-16 and VGG-19, are studied. And a dataset of 360 IVOCT images from 18 pullbacks are constructed to evaluate these features. Within these 360 images, 180 images are normal IVOCT images and the rest 180 images are IVOCT images with fibroatheroma. Here, one pullback belongs to one patient; leave-one-patient-out cross-validation is employed for evaluation. Data augmentation is applied on training set for each classification scheme. Linear support vector machine is conducted to classify the normal IVOCT image and IVOCT image with fibroatheroma. The experimental results show that deep features could achieve relatively high accuracy in fibroatheroma identification.
识别易损斑块在冠心病诊断中至关重要。最近出现的成像模态——血管内光学相干断层扫描（IVOCT），已被证明能够表征易损斑块的外观。与手动方法相比，自动识别纤维粥样斑块将更加高效和客观。深度卷积神经网络已被应用于许多医学图像分析任务中。在本文中，我们引入深度特征来解决纤维粥样斑块识别问题。研究了使用四种深度卷积神经网络（AlexNet、GoogLeNet、VGG - 16和VGG - 19）提取的深度特征。并构建了一个包含来自18次回撤的360幅IVOCT图像的数据集来评估这些特征。在这360幅图像中，180幅是正常的IVOCT图像，其余180幅是患有纤维粥样斑块的IVOCT图像。这里，一次回撤属于一名患者；采用留一患者交叉验证进行评估。对每个分类方案的训练集应用数据增强。使用线性支持向量机对正常IVOCT图像和患有纤维粥样斑块的IVOCT图像进行分类。实验结果表明，深度特征在纤维粥样斑块识别中能够达到相对较高的准确率。
