用深度学习生成高保真主动脉形状，支持虚拟临床试验。
Generative 3D Cardiac Shape Modelling for In-Silico Trials
- 将形状表示为神经符号距离场的零水平集，通过可训练嵌入向量编码几何特征。
- 在真实患者主动脉数据上训练，生成的新形状与实际解剖结构高度相似。
- 适合心血管仿真、个性化医疗和虚拟手术规划的研究人员使用。
我们提出一种基于深度学习的主动脉形状建模与生成方法，将形状表示为神经符号距离场的零水平集，并由一组可训练的嵌入向量条件化，以编码每种形状的几何特征。该网络在从CT图像重建的主动脉根部网格数据集上进行训练，通过使神经场在采样表面点处为零，并强制其空间梯度具有单位范数来实现。实验结果表明，该模型能够以高保真度表示主动脉形状。此外，通过对学习到的嵌入向量进行采样，可以生成与真实患者解剖结构相似的新形状，可用于虚拟临床试验。
原文摘要 · Abstract (English)
We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by a family of trainable embedding vectors with encode the geometric features of each shape. The network is trained on a dataset of aortic root meshes reconstructed from CT images by making the neural field vanish on sampled surface points and enforcing its spatial gradient to have unit norm. Empirical results show that our model can represent aortic shapes with high fidelity. Moreover, by sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies, which can be used for in-silico trials.
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