Congenital heart disease (CHD) encompasses a spectrum of cardiovascular structural abnormalities, often requiring customized treatment plans for individual patients. Computational modeling and analysis of these unique cardiac anatomies can improve diagnosis and treatment planning and may ultimately lead to improved outcomes. Deep learning (DL) methods have demonstrated the potential to enable efficient treatment planning by automating cardiac segmentation and mesh construction for patients with normal cardiac anatomies. However, CHDs are often rare, making it challenging to acquire sufficiently large patient cohorts for training such DL models. Generative modeling of cardiac anatomies has the potential to fill this gap via the generation of virtual cohorts; however, prior approaches were largely designed for normal anatomies and cannot readily capture the significant topological variations seen in CHD patients. Therefore, we propose a type- and shape-disentangled generative approach suitable to capture the wide spectrum of cardiac anatomies observed in different CHD types and synthesize differently shaped cardiac anatomies that preserve the unique topology for specific CHD types. Our DL approach represents generic whole heart anatomies with CHD type-specific abnormalities implicitly using signed distance fields (SDF) based on CHD type diagnosis. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. After training with a dataset containing the cardiac anatomies of 67 patients spanning 6 CHD types and 14 combinations of CHD types, our method successfully captures divergent anatomical variations across different types and the meaningful intermediate CHD states across the spectrum of related CHD diagnoses. Additionally, our method demonstrates superior performance in CHD anatomy generation in terms of CHD-type correctness and shape plausibility. It also exhibits comparable generalization performance when reconstructing unseen cardiac anatomies. Moreover, our approach shows potential in augmenting image-segmentation pairs for rarer CHD types to significantly enhance cardiac segmentation accuracy for CHDs. Furthermore, it enables the generation of CHD cardiac meshes for computational simulation, facilitating a systematic examination of the impact of CHDs on cardiac functions.

中文翻译：

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SDF4CHD：先天性心脏缺陷心脏解剖的生成模型


先天性心脏病 (CHD) 包括一系列心血管结构异常，通常需要针对个体患者制定定制的治疗计划。对这些独特的心脏解剖结构进行计算建模和分析可以改善诊断和治疗计划，并最终可能改善结果。深度学习（DL）方法已经证明了通过为具有正常心脏解剖结构的患者自动进行心脏分割和网格构建来实现有效治疗计划的潜力。然而，先心病通常很罕见，因此很难获得足够大的患者队列来训练此类深度学习模型。心脏解剖学的生成模型有可能通过生成虚拟队列来填补这一空白；然而，先前的方法主要是针对正常解剖结构而设计的，无法轻易捕获先心病患者中观察到的显着拓扑变化。因此，我们提出了一种类型和形状解开的生成方法，适合捕获在不同 CHD 类型中观察到的广泛心脏解剖结构，并合成不同形状的心脏解剖结构，从而保留特定 CHD 类型的独特拓扑。我们的 DL 方法隐含地使用基于 CHD 类型诊断的符号距离场 (SDF) 来表示具有 CHD 类型特异性异常的通用整个心脏解剖结构。为了捕获形状特定的变化，我们然后学习可逆变形，以改变所学习的 CHD 类型特定的解剖结构并重建患者特定的形状。 在使用包含 67 名患者心脏解剖结构（涵盖 6 种 CHD 类型和 14 种 CHD 类型组合）的数据集进行训练后，我们的方法成功捕获了不同类型之间的不同解剖变化以及相关 CHD 诊断范围内有意义的中间 CHD 状态。此外，我们的方法在 CHD 解剖生成中展示了在 CHD 类型正确性和形状合理性方面的卓越性能。在重建看不见的心脏解剖结构时，它还表现出相当的泛化性能。此外，我们的方法显示了增强罕见先心病类型的图像分割对的潜力，可显着提高先心病的心脏分割准确性。此外，它还能够生成用于计算模拟的 CHD 心脏网格，有助于系统检查 CHD 对心脏功能的影响。