用连续时空记忆网络实现心脏磁共振4D精准分割
Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation
- 基于时空记忆网络，利用心肌结构的时空连续性建模
- 在多个数据集上提升整体分割精度，尤其改善基底与心尖区域表现
- 适合需要全周期心脏影像分析的临床研究与辅助诊断
当前心脏电影磁共振（cMR）研究多聚焦于心动舒张末期（ED）和收缩末期（ES）阶段，忽视了整个序列中丰富的时序信息。这是因为全序列分割过程繁琐且易出错。传统方法先估计帧间运动场，再沿时间轴传播分割掩码，但该过程在基底和心尖切片中易产生误差，因这些区域存在显著的跨平面运动导致形态变化。受视频目标分割中时空记忆网络（STM）的启发，本文提出一种连续时空记忆（CSTM）网络，用于半监督下的全心、全序列cMR分割。该网络充分利用心肌结构在空间、尺度、时间及跨平面方向上的连续性先验，实现高效准确的4D分割。大量实验结果表明，本方法在多个cMR数据集上显著提升了4D分割性能，尤其在难以分割区域表现更优。
原文摘要 · Abstract (English)
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.
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