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聚焦于舒张末期与收缩末期时相，而忽视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运动场，进而沿时间轴传播掩码。然而，掩码传播结果易产生误差，尤其在基底与心尖层面——这些区域因跨平面运动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，用于半监督式全心及全序列心脏电影磁共振分割。该网络充分利用心脏解剖结构固有的空间、尺度、时序及跨平面连续性先验，实现精准高效的四维分割。跨多个心脏电影磁共振数据集的广泛实验结果表明，本方法能显著提升四维分割性能，特别是在难分割区域表现优异。