Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, LMU Munich, Germany.
Lab for Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, LMU Munich, Germany.
Med Image Anal. 2021 Jan;67:101852. doi: 10.1016/j.media.2020.101852. Epub 2020 Oct 10.
We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The networks operate on unordered point clouds and provide invariance to similarity transformations, avoiding the need to identify point correspondences between shapes. Based on the framework, we assemble a discriminative model for disease classification and age regression, as well as a generative model for the accruate reconstruction of shapes. In particular, we propose a conditional generative model, where the condition vector provides a mechanism to control the generative process. For instance, it enables to assess shape variations specific to a particular diagnosis, when passing it as side information. Next to working on single shapes, we introduce an extension for the joint analysis of multiple anatomical structures, where the simultaneous modeling of multiple structures can lead to a more compact encoding and a better understanding of disorders. We demonstrate the advantages of our framework in comprehensive experiments on real and synthetic data. The key insights are that (i) learning a shape representation specific to the given task yields higher performance than alternative shape descriptors, (ii) multi-structure analysis is both more efficient and more accurate than single-structure analysis, and (iii) point clouds generated by our model capture morphological differences associated to Alzheimer's disease, to the point that they can be used to train a discriminative model for disease classification. Our framework naturally scales to the analysis of large datasets, giving it the potential to learn characteristic variations in large populations.
我们引入了用于分析解剖形状的深度神经网络，该网络从给定任务中学习低维形状表示，而不是依赖于手工设计的表示。我们的框架是模块化的，由几个计算块组成，这些计算块执行基本的形状处理任务。这些网络作用于无序的点云，对相似变换具有不变性，避免了识别形状之间的点对应关系的需要。基于该框架，我们组装了一个用于疾病分类和年龄回归的判别模型，以及一个用于精确重建形状的生成模型。特别是，我们提出了一种条件生成模型，其中条件向量提供了一种控制生成过程的机制。例如，当将其作为辅助信息传递时，它可以评估特定于特定诊断的形状变化。除了处理单个形状外，我们还引入了一种用于多个解剖结构联合分析的扩展，其中对多个结构的同时建模可以导致更紧凑的编码和对疾病的更好理解。我们在真实和合成数据上的综合实验中证明了我们框架的优势。关键见解是：(i) 学习特定于给定任务的形状表示比替代形状描述符具有更高的性能；(ii) 多结构分析既比单结构分析更有效，也更准确；(iii) 我们的模型生成的点云捕捉到与阿尔茨海默病相关的形态差异，以至于它们可以用于训练用于疾病分类的判别模型。我们的框架自然适用于大规模数据集的分析，使其有可能从大量人群中学习特征变化。
