Automated segmentation of left ventricular cavity (LVC) in temporal cardiac image sequences (multiple time points) is a fundamental requirement for quantitative analysis of its structural and functional changes. Deep learning based methods for the segmentation of LVC are the state of the art; however, these methods are generally formulated to work on single time points, and fails to exploit the complementary information from the temporal image sequences that can aid in segmentation accuracy and consistency among the images across the time points. Furthermore, these segmentation methods perform poorly in segmenting the end-systole (ES) phase images, where the left ventricle deforms to the smallest irregular shape, and the boundary between the blood chamber and myocardium becomes inconspicuous. To overcome these limitations, we propose a new method to automatically segment temporal cardiac images where we introduce a spatial sequential (SS) network to learn the deformation and motion characteristics of the LVC in an unsupervised manner; these characteristics were then integrated with sequential context information derived from bi-directional learning (BL) where both chronological and reverse-chronological directions of the image sequence were used. Our experimental results on a cardiac computed tomography (CT) dataset demonstrated that our spatial-sequential network with bi-directional learning (SS-BL) method outperformed existing methods for LVC segmentation. Our method was also applied to MRI cardiac dataset and the results demonstrated the generalizability of our method.

翻译：在时序心脏图像序列（多时间点）中对左心室腔进行自动分割，是定量分析其结构和功能变化的基础要求。基于深度学习的左心室腔分割方法代表了当前技术水平；然而，这些方法通常被设计用于处理单一时间点，未能利用时序图像序列中的互补信息，而这些信息有助于提高分割精度并确保跨时间点图像间的一致性。此外，这些分割方法在处理收缩末期相位图像时表现不佳，此时左心室变形为最小的不规则形状，血液腔室与心肌之间的边界变得不明显。为克服这些局限，我们提出了一种自动分割时序心脏图像的新方法，其中引入了一种空间序列网络，以无监督方式学习左心室腔的形变与运动特征；随后将这些特征与来自双向学习的序列上下文信息相结合，其中同时利用了图像序列的顺时序和逆时序方向。我们在心脏计算机断层扫描数据集上的实验结果表明，我们提出的结合双向学习的空间序列网络方法在左心室腔分割任务上优于现有方法。我们的方法亦应用于磁共振成像心脏数据集，结果证明了该方法的泛化能力。