Abstract
Accurate motion estimation in cardiac computed tomography (CT) imaging is critical for assessing cardiac function and surgical planning. Data-driven methods have become the standard approach for dense motion estimation, but they rely on vast amounts of labeled data with dense ground-truth (GT) motion annotations, which are often unfeasible to obtain. To address this limitation, we present a novel approach that synthesizes realistically looking pairs of cardiac CT frames enriched with dense 3D flow field annotations. Our method leverages a conditional Variational Autoencoder (CVAE), which incorporates a novel multi-scale feature conditioning mechanism and is trained to generate 3D flow fields conditioned on a single CT frame. By applying the generated flow field to warp the given frame, we create pairs of frames that simulate realistic myocardium deformations across the cardiac cycle. These pairs serve as fully annotated data samples, providing optical flow GT annotations. Our data generation pipeline could enable the training and validation of more complex and accurate myocardium motion models, allowing for substantially reducing reliance on manual annotations. Our code, along with animated generated samples and additional material, is available on our project page: this https URL.
Abstract (translated)
在心脏计算机断层扫描（CT）成像中，准确的运动估计对于评估心脏功能和手术规划至关重要。数据驱动方法已成为密集运动估算的标准方法，但它们依赖于大量带有密集真实标签（GT）运动注释的数据集，这些数据往往难以获取。为了解决这一限制，我们提出了一种新方法，该方法通过生成具有密集3D流场标注的真实外观心脏CT帧对来合成数据。 我们的方法利用了条件变分自动编码器（CVAE），并引入了一个新颖的多尺度特征调节机制，使其能够根据单个CT帧生成3D流场。通过应用生成的流场来变形给定的帧，我们创建了一对模拟心脏周期中真实心肌变形的帧。这些配对作为完全标注的数据样本，提供光学流GT注释。 我们的数据生成流水线可以促进更复杂和准确的心肌运动模型的训练和验证，从而大大减少对手动注释的依赖。我们的代码、动画生成示例和其他材料可在项目页面上获取：this https URL。
URL
https://arxiv.org/abs/2507.15894
