Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac cine sequencing provides a basis to quantify cardiac measures such as ejection fraction. A method is presented that curtails the expense and observer bias of manual cardiac evaluation by combining semantic segmentation and disease classification into a fully automatic processing pipeline. The initial processing element consists of a robust dilated convolutional neural network architecture for voxel-wise segmentation of the myocardium and ventricular cavities. The resulting comprehensive volumetric feature matrix captures diagnostic clinical procedure data and is utilized by the final processing element to model a cardiac pathology classifier. Our approach evaluated anonymized cardiac images from a training data set of 100 patients (4 pathology groups, 1 healthy group, 20 patients per group) examined at the University Hospital of Dijon. The top average Dice index scores achieved were 0.940, 0.886, and 0.849 for structure segmentation of the left ventricle (LV), myocardium, and right ventricle (RV), respectively. A 5-ary pathology classification accuracy of 90% was recorded on an independent test set using the trained model. Performance results demonstrate the potential for advanced machine learning methods to deliver accurate, efficient, and reproducible cardiac pathological assessment.

中文翻译：

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使用语义分割和集成学习的自动心血管病理学评估。

心脏磁共振成像提供高空间分辨率，能够改进对心血管疾病分期的重要功能和形态特征的提取。心脏电影测序中心室腔和心肌的分割为量化心脏测量值（如射血分数）提供了基础。提出了一种方法，该方法通过将语义分割和疾病分类结合到全自动处理管道中来减少人工心脏评估的费用和观察者偏差。初始处理元素由一个健壮的扩张卷积神经网络架构组成，用于心肌和心室腔的体素分割。得到的综合体积特征矩阵捕获诊断临床程序数据，并由最终处理元素用于对心脏病理分类器进行建模。我们的方法评估了来自第戎大学医院检查的 100 名患者（4 个病理组，1 个健康组，每组 20 名患者）的训练数据集的匿名心脏图像。对于左心室 (LV)、心肌和右心室 (RV) 的结构分割，获得的最高平均 Dice 指数分数分别为 0.940、0.886 和 0.849。使用训练模型在独立测试集上记录了 90% 的 5 元病理分类准确率。性能结果表明，先进的机器学习方法具有提供准确、高效和可重复的心脏病理评估的潜力。