Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera, s/n, 46022 Valencia, Spain.
Unidad de Imagen Cardíaca, ERESA-ASCIRES Grupo Biomédico, Valencia, Spain.
Comput Methods Programs Biomed. 2021 Sep;208:106275. doi: 10.1016/j.cmpb.2021.106275. Epub 2021 Jul 9.
Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explainability to the estimated value.
The network was designed to directly target the volumes to estimate, not requiring any labeled segmentation on the images. The network was based on a 3D U-net with extra layers defined in a scanning module that learned features like the circularity of the objects and the volumes to estimate in a weakly-supervised manner. The only targets defined were the left ventricle volumes and the circularity of the object detected through the estimation of the π value derived from its shape. We had access to 397 cases corresponding to 397 different subjects. We randomly selected 98 cases to use as test set.
The results show a good match between the real and estimated volumes in the test set, with a mean relative error of 8% and a mean absolute error of 9.12 ml with a Pearson correlation coefficient of 0.95. The derived segmentations obtained by the network achieved Dice coefficients with a mean value of 0.79.
The proposed method is capable of obtaining the left ventricle volume biomarker in the end-diastole and offer an explanation of how it obtains the result in the form of a segmentation mask without the need of segmentation labels to train the algorithm, making it a potentially more trustworthy method for clinicians and a way to train neural networks more easily when segmentation labels are not readily available.
磁共振成像是评估心脏最可靠的成像技术。更具体地说，左心室的分析非常重要，因为主要的病变直接影响到这个区域。为了描述左心室，有必要提取其体积。在这项工作中，我们提出了一种神经网络架构，能够直接估计短轴电影磁共振成像舒张末期帧的左心室体积，并提供体积计算基础的区域分割，从而为估计值提供可解释性。
该网络旨在直接针对要估计的体积，而不需要对图像进行任何标记分割。该网络基于一个 3D U-net，具有在扫描模块中定义的额外层，该模块以弱监督的方式学习对象的圆形度和要估计的体积等特征。定义的唯一目标是左心室体积和通过估计其形状得出的π值检测到的对象的圆形度。我们获得了 397 例对应于 397 个不同个体的病例。我们随机选择了 98 例作为测试集。
结果表明，在测试集中，真实和估计的体积之间有很好的匹配，平均相对误差为 8%，平均绝对误差为 9.12ml，皮尔逊相关系数为 0.95。网络得到的分割结果的 Dice 系数平均值为 0.79。
所提出的方法能够获得舒张末期的左心室体积生物标志物，并以分割掩模的形式提供其获得结果的解释，而不需要分割标签来训练算法，这使得它成为临床医生更可信的方法，并且在没有分割标签的情况下更容易训练神经网络。
