Fan Yonghui, Wang Yalin
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, USA.
Med Image Comput Comput Assist Interv. 2020;12264:786-796. doi: 10.1007/978-3-030-59719-1_76. Epub 2020 Sep 29.
The anatomical landmarking on statistical shape models is widely used in structural and morphometric analyses. The current study focuses on leveraging geometric features to realize an automatic and reliable landmarking. The existing implementations usually rely on classical geometric features and data-driven learning methods. However, such designs often have limitations to specific shape types. Additionally, calculating the features as a standalone step increases the computational cost. In this paper, we propose a convolutional Bayesian model for anatomical landmarking on multi-dimensional shapes. The main idea is to embed the convolutional filtering in a stationary kernel so that the geometric features are efficiently captured and implicitly encoded into the prior knowledge of a Gaussian process. In this way, the posterior inference is geometrically meaningful without entangling with extra features. By using a Gaussian process regression framework and the active learning strategy, our method is flexible and efficient in extracting arbitrary numbers of landmarks. We demonstrate extensive applications on various publicly available datasets, including one brain imaging cohort and three skeletal anatomy datasets. Both the visual and numerical evaluations verify the effectiveness of our method in extracting significant landmarks.
统计形状模型上的解剖学地标定位在结构和形态计量分析中被广泛应用。当前的研究专注于利用几何特征来实现自动且可靠的地标定位。现有的实现方式通常依赖于经典几何特征和数据驱动的学习方法。然而，这样的设计往往对特定形状类型存在局限性。此外，将特征计算作为一个独立步骤会增加计算成本。在本文中，我们提出了一种用于多维形状解剖学地标定位的卷积贝叶斯模型。主要思想是将卷积滤波嵌入到一个平稳核中，以便有效地捕获几何特征并将其隐式编码到高斯过程的先验知识中。通过这种方式，后验推断在几何上是有意义的，而无需与额外特征纠缠。通过使用高斯过程回归框架和主动学习策略，我们的方法在提取任意数量的地标时灵活且高效。我们在各种公开可用的数据集上展示了广泛的应用，包括一个脑成像队列和三个骨骼解剖数据集。视觉和数值评估均验证了我们的方法在提取重要地标方面的有效性。
