标题： CineMyoPS：从电影心脏MRI中分割心肌病变 显示英文标题

标题： CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR

摘要： 心肌梗死（MI）是全球死亡的主要原因。 晚期钆增强（LGE）和T2加权心脏磁共振（CMR）成像可以分别识别瘢痕和水肿区域，这两者对于MI的风险分层和预后评估都是必不可少的。 尽管结合多序列CMR的互补信息是有用的，但获取这些序列可能耗时且成本高昂，例如由于需要注射对比剂。 电影CMR是一种快速且无需对比剂的成像技术，可以可视化由急性MI引起的心肌运动和结构异常。 因此，我们提出了一种新的端到端深度神经网络，称为CineMyoPS，仅从电影CMR图像中分割心肌病变，\ie 瘢痕和水肿。 具体来说， CineMyoPS提取与MI相关的心肌运动和解剖特征。 鉴于这些特征之间的相互依赖性，我们设计了一个一致性损失（类似于协同训练策略）以促进它们的联合学习。 此外，我们提出了一种时间序列聚合策略，以整合心脏周期中的MI相关特征，从而提高心肌病变的分割准确性。 在多中心数据集上的实验结果表明，CineMyoPS在心肌病变分割、运动估计和解剖分割方面表现出色。 显示英文摘要

摘要： Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR) imaging can respectively identify scarring and edema areas, both of which are essential for MI risk stratification and prognosis assessment. Although combining complementary information from multi-sequence CMR is useful, acquiring these sequences can be time-consuming and prohibitive, e.g., due to the administration of contrast agents. Cine CMR is a rapid and contrast-free imaging technique that can visualize both motion and structural abnormalities of the myocardium induced by acute MI. Therefore, we present a new end-to-end deep neural network, referred to as CineMyoPS, to segment myocardial pathologies, \ie scars and edema, solely from cine CMR images. Specifically, CineMyoPS extracts both motion and anatomy features associated with MI. Given the interdependence between these features, we design a consistency loss (resembling the co-training strategy) to facilitate their joint learning. Furthermore, we propose a time-series aggregation strategy to integrate MI-related features across the cardiac cycle, thereby enhancing segmentation accuracy for myocardial pathologies. Experimental results on a multi-center dataset demonstrate that CineMyoPS achieves promising performance in myocardial pathology segmentation, motion estimation, and anatomy segmentation.