发表机构
College of Computing and Data Science, Nanyang Technological University; Polytechnique Montreal（南洋理工大学计算与数据科学学院; 蒙特利尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出半监督时空知识蒸馏框架，利用循环瓶颈将空间教师模型知识迁移到时空学生模型，实现主动脉追踪准确率与可靠性提升，减少结构异常超56%。
AI 中文摘要
心脏电影MRI通过捕获主动脉的连续壁运动，作为心血管血流动力学的直接视觉指标。量化心动周期中这些动态结构变化对于测量主动脉扩张性至关重要，主动脉扩张性是动脉僵硬度的主要标志物。然而，标准2D分割网络独立关注每一帧，因此当快速收缩期血流暂时遮挡主动脉边界时，这种缺乏连续上下文的情况会导致帧间追踪丢失和边界不一致。时空（2D+t）网络可在序列中强制时间一致性，但面临专家注释稀缺的问题。为解决此问题，我们提出一种利用心动周期的半监督时空（2D到2D+t）知识蒸馏框架，该框架通过执行动态潜在拦截将空间教师模型的专业知识蒸馏到时空学生网络，该动态潜在拦截将循环时空瓶颈与残差空间旁路配对。我们的模型选择策略在选择最大化解剖一致性的epoch前，应用基线验证阈值（DSC≥0.50）。该策略使时空学生模型实现了优异的表面追踪准确率（NSD@1mm=92.3%±0.2%）和高结构可靠性（Frac₂CC=99.2%±0.6%），与2D nnU-Net基线相比，减少了全人群范围的结构异常超过56%。
英文摘要
Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying these dynamic structural changes across the cardiac cycle is essential for measuring aortic distensibility, a primary marker of arterial stiffness. However, standard 2D segmentation networks focus on each frame independently. Consequently, when rapid systolic flow temporarily obscures the aorta's boundaries, this lack of continuous context results in frame-to-frame tracking dropouts and boundary inconsistencies. Spatiotemporal ($2\text{D}+t$) networks can enforce temporal consistency across the sequence but suffer from a scarcity of expert annotations. To address this, we present a semi-supervised spatiotemporal ($2\text{D}$ to $2\text{D}+t$) knowledge distillation framework exploiting the cardiac cycle. The framework distills a spatial teacher's expertise into a spatiotemporal student network by executing a dynamic latent interception, pairing a recurrent spatiotemporal bottleneck with a residual spatial bypass. Our model selection strategy applies a baseline validation threshold ($\text{DSC} \ge 0.50$) prior to selecting the epoch that maximizes anatomical consistency. This strategy enables the spatiotemporal student model to achieve superior surface tracking accuracy ($\text{NSD@1mm} = 92.3\% \pm 0.2\%$) and high structural reliability ($\text{Frac}_{2\text{CC}} = 99.2\% \pm 0.6\%$), reducing population-wide structural anomalies by over 56\% compared to a 2D nnU-Net baseline.
CommentsAccepted at the 17th Statistical Atlases and Computational Models of the Heart (STACOM) Workshop, MICCAI 2026. Springer LNCS
