SDF4CHD：先天性心脏缺陷心脏解剖结构的生成式建模
SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects
AI总结：
针对先天性心脏病（CHD）样本稀缺、现有生成模型难以捕捉其拓扑变异的问题，提出SDF4CHD类型与形状解耦生成方法，用SDF隐式表示不同CHD类型解剖结构并学习可逆形变，可扩充分割数据、生成仿真用网格队列。
摘要：
先天性心脏病（CHD）涵盖一系列心血管结构异常，通常需要为每位患者制定定制化治疗方案。对这些独特心脏解剖结构进行计算建模与分析，能够优化诊断与治疗规划，并最终有望改善治疗结局。深度学习（DL）方法已被证明可通过自动化正常心脏解剖结构患者的心脏分割与网格构建，实现高效的治疗规划。然而，先天性心脏病通常较为罕见，难以获取足够大的患者队列来训练此类深度学习模型。心脏解剖结构的生成式建模有望通过生成虚拟队列填补这一空白；但现有方法大多针对正常解剖结构设计，无法有效捕捉先天性心脏病患者中存在的显著拓扑变异。因此，我们提出一种类型与形状解耦的生成式方法，适用于捕捉不同先天性心脏病类型中广泛存在的心脏解剖结构变异，并能合成保留特定先天性心脏病类型独特拓扑结构的不同形状心脏解剖结构。我们的深度学习方法基于先天性心脏病类型诊断，使用符号距离场（SDF）隐式表示带有先天性心脏病类型特异性异常的通用全心解剖结构，该方法可便捷捕捉不同类型间的解剖结构差异，并表征有意义的先天性心脏病中间状态。为捕捉形状特异性变异，我们随后学习可逆形变，对习得的先天性心脏病类型特异性解剖结构进行形态变换，重建患者特异性形状。我们的方法有望为较罕见先天性心脏病类型扩充图像-分割配对数据以用于心脏分割，并生成先天性心脏病心脏网格队列以用于计算仿真。
英文摘要：
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, which conveniently captures divergent anatomical variations across different types and represents meaningful intermediate CHD states. To capture the shape-specific variations, we then learn invertible deformations to morph the learned CHD type-specific anatomies and reconstruct patient-specific shapes. Our approach has the potential to augment the image-segmentation pairs for rarer CHD types for cardiac segmentation and generate cohorts of CHD cardiac meshes for computational simulation.
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。
