Abstract
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.
Abstract (translated)
当前的心脏动态磁共振成像（cMR）研究主要关注舒张末期（ED）和收缩末期（ES）的相位，而忽略了整个图像序列中丰富的时序信息。这是由于目前对整个序列进行分割是一个繁琐且不准确的过程。传统的方法首先估计帧之间的运动场，然后使用该运动场沿时间轴传播掩模。然而，这种掩模传播的结果容易出错，尤其是在基底和心尖切片上，因平面外的运动会导致心脏周期中的形态和结构发生显著变化。受到基于时空记忆（STM）网络在视频对象分割（VOS）方面最新进展的启发，我们提出了一种连续时空记忆（CSTM）网络用于半监督下的整个心脏及整段序列cMR分割。我们的CSTM网络充分利用了潜在心肌解剖结构的空间、尺度、时序和平面外延续性先验知识，以实现准确且快速的4D分割。在多个cMR数据集上的广泛实验结果表明，我们提出的方法能够提高4D cMR分割性能，特别是对于难以分割的区域有显著改善。
URL
https://arxiv.org/abs/2410.23191
