A Point Distribution Model (PDM) is the basis of a Statistical Shape Model (SSM) that relies on a set of landmark points to represent a shape and characterize the shape variation. In this work, we present a self-supervised approach to extract landmark points from a given registration model for the PDMs. Based on the assumption that the landmarks are the points that have the most influence on registration, existing works learn a point-based registration model with a small number of points to estimate the landmark points that influence the deformation the most. However, such approaches assume that the deformation can be captured by point-based registration and quality landmarks can be learned solely with the deformation capturing objective. We argue that data with complicated deformations can not easily be modeled with point-based registration when only a limited number of points is used to extract influential landmark points. Further, landmark consistency is not assured in existing approaches In contrast, we propose to extract landmarks based on a given registration model, which is tailored for the target data, so we can obtain more accurate correspondences. Secondly, to establish the anatomical consistency of the predicted landmarks, we introduce a landmark discovery loss to explicitly encourage the model to predict the landmarks that are anatomically consistent across subjects. We conduct experiments on an osteoarthritis progression prediction task and show our method outperforms existing image-based and point-based approaches.

翻译：点分布模型（PDM）是统计形状模型（SSM）的基础，它依赖于一组地标点来表示形状并刻画形状变化。本文提出了一种自监督方法，用于从给定的配准模型中为PDM提取地标点。现有工作基于地标点对配准影响最大的假设，通过使用少量点学习基于点的配准模型来估计对变形影响最大的地标点。然而，这类方法假设变形可由基于点的配准捕获，且仅通过变形捕获目标即可学习到高质量地标点。我们认为，当仅使用有限数量的点来提取关键地标点时，具有复杂形变的数据难以通过基于点的配准建模。此外，现有方法无法保证地标点的一致性。相比之下，我们提出基于针对目标数据定制的配准模型来提取地标点，从而获得更精确的对应关系。其次，为建立预测地标点的解剖一致性，我们引入地标发现损失函数，明确鼓励模型预测跨主体解剖结构一致的地标点。我们在骨关节炎进展预测任务上进行实验，结果表明我们的方法优于现有基于图像和基于点的方法。