Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.

翻译：心肌病理分割对于心肌梗死的风险分层和治疗规划至关重要。多序列心脏磁共振图像能够提供宝贵信息：平衡稳态自由进动电影序列可清晰呈现解剖边界，而钆剂延迟增强和T2加权心脏磁共振序列分别显示心肌梗死后的瘢痕与水肿组织。现有方法通常融合不同心脏磁共振序列的解剖与病理信息进行心肌病理分割，但均假设这些图像已实现空间对齐。然而，临床实践中因呼吸运动导致多序列心脏磁共振图像通常未对齐，这为心肌病理分割带来了额外挑战。本文提出一种面向未对齐多序列心脏磁共振图像的自动心肌病理分割框架。具体而言，我们设计了一个联合计算模型，同步进行图像配准与信息融合，将多序列特征聚合到公共空间以提取解剖结构（即心肌）。进而，通过提取的心肌突出公共空间中的信息区域，结合心肌病理与心肌的空间关系来提升心肌病理分割性能。在私有多序列心脏磁共振数据集及MYOPS2020挑战赛公开数据集上的实验表明，我们的框架在全自动心肌病理分割任务中能取得令人满意的性能。