论文标题
CNN-CASS：CNN用于MPR图像中冠状动脉狭窄评分的分类
CNN-CASS: CNN for Classification of Coronary Artery Stenosis Score in MPR Images
论文作者
论文摘要
为了减少患者的等待时间诊断冠状动脉疾病，使用自动方法使用冠状动脉层析扫描扫描或提取的多平台重建（MPR）图像来识别其严重程度，从而使医生在每种情况的优先级中获得第二次开胃。以前研究的主要缺点是缺乏可以保证其可靠性的大量数据。另一个限制是使用手动预处理的手工制作功能，例如中心线提取。我们通过基于Shufflenet V2网络体系结构应用不同的自动化方法来克服这两种限制，并在建议的MPR图像的收集数据集上测试它，该数据集比以前在该字段中使用的任何其他方法都要大。我们还分别使用708和105例患者的整个弯曲MPR图像省略了中心线提取步骤，并训练和测试我们的模型。该模型预测了三个类别之一：正常的“无狭窄”，“无显着” - 检测到的狭窄的1-50％，“显着” - 超过50％的狭窄。我们通过可视化网络选择的最重要功能来证明模型的解释性。对于狭窄得分分类，该方法与以前的作品相比显示出改善的性能，在患者水平上达到了80％的精度。我们的代码公开可用。
To decrease patient waiting time for diagnosis of the Coronary Artery Disease, automatic methods are applied to identify its severity using Coronary Computed Tomography Angiography scans or extracted Multiplanar Reconstruction (MPR) images, giving doctors a second-opinion on the priority of each case. The main disadvantage of previous studies is the lack of large set of data that could guarantee their reliability. Another limitation is the usage of handcrafted features requiring manual preprocessing, such as centerline extraction. We overcome both limitations by applying a different automated approach based on ShuffleNet V2 network architecture and testing it on the proposed collected dataset of MPR images, which is bigger than any other used in this field before. We also omit centerline extraction step and train and test our model using whole curved MPR images of 708 and 105 patients, respectively. The model predicts one of three classes: 'no stenosis' for normal, 'non-significant' - 1-50% of stenosis detected, 'significant' - more than 50% of stenosis. We demonstrate model's interpretability through visualization of the most important features selected by the network. For stenosis score classification, the method shows improved performance comparing to previous works, achieving 80% accuracy on the patient level. Our code is publicly available.
