Background and objective

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

---

通过深度学习弱监督方法具有可解释性的自动左心室容积计算

背景和目的

磁共振成像是评估心脏的最可靠的成像技术。更具体地说，左心室的分析非常重要，因为主要病理直接影响该区域。为了表征左心室，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，该架构能够直接估计舒张末期短轴电影磁共振成像中的左心室容积，并提供作为容积计算基础的区域分割，从而提供估计值的可解释性。

方法

该网络旨在直接针对要估计的体积，不需要对图像进行任何标记分割。该网络基于 3D U-net，在扫描模块中定义了额外的层，该模块学习了物体的圆形度和体积等特征，以弱监督的方式进行估计。唯一定义的目标是左心室体积和通过估计从其形状导出的 π 值检测到的对象的圆形度。我们可以访问对应于 397 个不同主题的 397 个案例。我们随机选择了 98 个案例作为测试集。

结果

结果显示测试集中真实体积和估计体积之间的匹配良好，平均相对误差为 8%，平均绝对误差为 9.12 ml，Pearson 相关系数为 0.95。由网络获得的派生分割实现了平均值为 0.79 的 Dice 系数。

结论

所提出的方法能够在舒张末期获得左心室容积生物标志物，并解释它如何以分割掩码的形式获得结果，而无需分割标签来训练算法，使其成为一个潜在的更多临床医生值得信赖的方法，以及当分割标签不容易获得时更容易训练神经网络的方法。