Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA; Department of Pediatrics, Stanford University, Stanford, CA, USA; Cardiovascular Institute, Stanford University, Stanford, CA, USA.
Department of Radiology, Stanford University, Stanford, CA, USA; Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland.
Med Image Anal. 2024 Oct;97:103293. doi: 10.1016/j.media.2024.103293. Epub 2024 Aug 8.
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.
先天性心脏病（CHD）涵盖了一系列心血管结构异常，通常需要为个体患者制定定制化的治疗计划。对这些独特心脏解剖结构的计算建模和分析可以改善诊断和治疗计划，并最终导致更好的结果。深度学习（DL）方法已经证明了通过自动化正常心脏解剖患者的心脏分割和网格构建来实现高效治疗计划的潜力。然而，CHD 通常很少见，因此难以获得足够大的患者队列来训练这种 DL 模型。心脏解剖的生成式建模具有通过生成虚拟队列来填补这一空白的潜力；然而，先前的方法主要是为正常解剖设计的，并且不能轻易捕获 CHD 患者中看到的显著拓扑变化。因此，我们提出了一种类型和形状分离的生成式方法，适用于捕获不同 CHD 类型中观察到的广泛的心脏解剖结构，并合成不同形状的心脏解剖结构，为特定的 CHD 类型保留独特的拓扑。我们的 DL 方法使用基于 CHD 类型诊断的基于符号距离场（SDF）的隐式表示具有 CHD 类型特异性异常的通用整个心脏解剖结构。为了捕获特定于形状的变化，我们然后学习可变形的变形来变形学习的 CHD 类型特异性解剖结构并重建患者特异性形状。在使用包含 67 名患者的心脏解剖数据集进行训练后，该数据集涵盖了 6 种 CHD 类型和 14 种 CHD 类型组合，我们的方法成功地捕获了不同类型之间的发散解剖变化以及整个相关 CHD 诊断范围内有意义的中间 CHD 状态。此外，我们的方法在 CHD 解剖生成方面表现出出色的性能，在 CHD 类型正确性和形状合理性方面表现出色。当重建看不见的心脏解剖结构时，它也表现出相当的泛化性能。此外，我们的方法在增强罕见 CHD 类型的图像分割对方面显示出潜力，从而显著提高 CHD 的心脏分割准确性。此外，它还可以生成用于计算模拟的 CHD 心脏网格，从而可以系统地检查 CHD 对心脏功能的影响。
