Transforming Heart Chamber Imaging：面向全心重建与分割的自监督学习
Transforming Heart Chamber Imaging: Self-Supervised Learning for Whole Heart Reconstruction and Segmentation
AI总结：
针对CMR长轴四腔视图全心分割难题，本文提出二维和三维两阶段自监督混合Transformer与CNN架构，提升心腔分割及四腔室网格重建性能。
摘要：
心脏磁共振（Cardiac Magnetic Resonance，CMR）的自动分割在高效评估心功能方面发挥着关键作用，可提供快速临床评估，使医疗从业者和患者均受益。尽管近期研究主要集中于描绘短轴方向上的结构，但对长轴表示的关注较少，这主要是由于该方向上结构的复杂性质。在二维稳态自由进动（steady-state free precession，SSFP）电影序列中，对左心室（left ventricular，LV）心肌和四个心腔执行逐像素分割，是多种分析的关键预处理阶段。然而，挑战在于不同患者、临床视图、扫描仪和成像方案之间，心脏在对比度、外观、方向和位置方面存在显著差异。因此，在这种情境下实现全自动语义分割是众所周知的难题。近年来，已有若干深度学习模型被提出，用于准确量化和诊断心脏病变。这些自动化工具高度依赖磁共振图像（MRI）中心脏结构的准确分割。因此，需要新的方法来处理此类结构的几何和纹理复杂性。我们提出了用于4CH全心分割的二维和三维两阶段自监督深度学习分割混合 transformer 与基于 CNN 的架构。在4CH视图中准确分割心室和心房，对于分析心脏健康和重建四腔室网格至关重要，而这些网格对于估计评估整体心脏状况的各种参数必不可少。我们提出的方法优于最先进技术，展示了在该领域中的卓越性能。
英文摘要：
Automated segmentation of Cardiac Magnetic Resonance (CMR) plays a pivotal role in efficiently assessing cardiac function, offering rapid clinical evaluations that benefit both healthcare practitioners and patients. While recent research has primarily focused on delineating structures in the short-axis orientation, less attention has been given to long-axis representations, mainly due to the complex nature of structures in this orientation. Performing pixel-wise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady-state free precession (SSFP) cine sequences is a crucial preprocessing stage for various analyses. However, the challenge lies in the significant variability in contrast, appearance, orientation, and positioning of the heart across different patients, clinical views, scanners, and imaging protocols. Consequently, achieving fully automatic semantic segmentation in this context is notoriously challenging. In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in magnetic resonance images (MRI). Hence, there is a need for new methods to handle such structures' geometrical and textural complexities. We proposed 2D and 3D two-stage self-supervised deep learning segmentation hybrid transformer and CNN-based architectures for 4CH whole heart segmentation. Accurate segmentation of the ventricles and atria in 4CH views is crucial for analyzing heart health and reconstructing four-chamber meshes, which are essential for estimating various parameters to assess overall heart condition. Our proposed method outperformed state-of-the-art techniques, demonstrating superior performance in this domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
