Current cardiac cine magnetic resonance image (cMR) studies focus on the end diastole (ED) and end systole (ES) phases, while ignoring the abundant temporal information in the whole image sequence. This is because whole sequence segmentation is currently a tedious process and inaccurate. Conventional whole sequence segmentation approaches first estimate the motion field between frames, which is then used to propagate the mask along the temporal axis. However, the mask propagation results could be prone to error, especially for the basal and apex slices, where through-plane motion leads to significant morphology and structural change during the cardiac cycle. Inspired by recent advances in video object segmentation (VOS), based on spatio-temporal memory (STM) networks, we propose a continuous STM (CSTM) network for semi-supervised whole heart and whole sequence cMR segmentation. Our CSTM network takes full advantage of the spatial, scale, temporal and through-plane continuity prior of the underlying heart anatomy structures, to achieve accurate and fast 4D segmentation. Results of extensive experiments across multiple cMR datasets show that our method can improve the 4D cMR segmentation performance, especially for the hard-to-segment regions.

翻译：当前心脏电影磁共振成像研究主要关注舒张末期与收缩末期时相，而忽略了完整图像序列中丰富的时序信息。这是因为全序列分割目前仍是一个繁琐且不精确的过程。传统的全序列分割方法首先估计帧间的运动场，然后利用该运动场沿时间轴传播掩码。然而，掩码传播结果容易产生误差，尤其在基底与心尖层面，平面外运动会导致心脏周期内显著的形态与结构变化。受基于时空记忆网络的视频目标分割最新进展启发，我们提出一种连续时空记忆网络，用于半监督全心脏、全序列心脏电影磁共振成像分割。我们的连续时空记忆网络充分利用了心脏解剖结构固有的空间、尺度、时序及平面外连续性先验，以实现精准高效的四维分割。跨多个心脏电影磁共振成像数据集的广泛实验结果表明，本方法能显著提升四维心脏电影磁共振成像分割性能，特别是在难以分割的区域。