Pereira Renato F, Rebelo Marina S, Moreno Ramon A, Marco Anderson G, Lima Daniel M, Arruda Marcelo A F, Krieger Jose E, Gutierrez Marco A
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1221-1224. doi: 10.1109/EMBC44109.2020.9176166.
Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction.Clinical Relevance- With the proposed method, it is possible to perform automatically the full quantification of regional clinically relevant parameters of the left ventricle in short-axis CMRI images with superior performance compared to state-of-the-art methods.
心血管磁共振成像（CMRI）是评估心脏功能，尤其是左心室（LV）功能的最准确的非侵入性检查方法之一。在这种检查方法中，目前专家手动或半自动勾勒左心室轮廓是腔室分割的标准临床做法。尽管如此，左心室的整体量化仍然是一个挑战。在这项工作中，描述了两种卷积神经网络（CNN）架构相结合用于左心室定量评估的方法，该方法可估计腔室和心肌面积、三个方向的心内膜腔尺寸以及六个径向方向的心肌区域壁厚。该方法在56例患者的CMRI检查（LVQuan19数据集）中得到验证，并通过Dice指数、平均绝对误差和相关性指标进行评估，与现有方法相比具有更优的性能。CNN架构的结合提供了一种更简单但完全自动化的方法，无需专家干预。临床意义——使用所提出的方法，可以在短轴CMRI图像中自动对左心室的区域临床相关参数进行全面量化，与现有方法相比具有更优的性能。
