Abstract
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and to answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves a high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data.
Abstract (translated)
在心脏图像分析中,有两个关键问题。这些问题分别是从图像中评估心脏的结构和运动;以及理解它们如何与性别、年龄和疾病等非成像临床因素相关联。虽然第一个问题通常可以通过图像分割和运动跟踪算法来解决,但我们模型能力和回答第二个问题仍然有限。在这个工作中,我们提出了一种新的条件生成模型,以描述心脏的4D时间和空间结构,及其与非成像临床因素的互动。临床因素被整合作为生成模型的条件,这使我们能够研究这些因素如何影响心脏结构。我们主要评估模型性能的两个任务是,结构序列完成和序列生成。模型在结构序列完成方面表现优异,与或超越了其他先进的生成模型。在序列生成方面,给定临床条件,模型可以生成与现实数据共享相似分布的真实的4D序列结构。
URL
https://arxiv.org/abs/2301.13098
