Porto Arthur, Rolfe Sara, Maga A Murat
Department of Biological Sciences Louisiana State University Baton Rouge LA USA.
Center for Computation and Technology Louisiana State University Baton Rouge LA USA.
Methods Ecol Evol. 2021 Nov;12(11):2129-2144. doi: 10.1111/2041-210X.13689. Epub 2021 Aug 9.
Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional (3D) morphometric data are still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system specificity and its overall speed, have prevented its wide dissemination.Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect high-dimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structure's surface. Second, it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied at the intraspecific level to any biological structure of interest, regardless of whether anatomical atlases are available.Our validation procedures indicate that the method can recover intraspecific patterns of morphological variation that are largely comparable to those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation.The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a SlicerMorph module. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.
基于地标点的几何形态测量学已成为生态学和进化领域中进行大小和形状定量分析的一门重要学科。随着数字化地标点密度的不断增加，开发一种全自动地标点放置方法的可能性引起了广泛关注。尽管图像配准技术最近取得了进展，这可能为自动化提供一条途径，但三维（3D）形态测量数据仍主要由训练有素的专家收集。在很大程度上，基于图像配准所需的大型基础设施要求，及其系统特异性和整体速度，阻碍了其广泛传播。在此，我们提出并实施了一种通用且轻量级的基于点云的方法，用于在3D表面自动收集高维地标点数据（通过点云对齐和对应分析实现自动地标点标记）。与基于图像的方法相比，我们的框架具有几个优点。首先，尽管它依赖于单个随机参考标本且对结构表面的采样要稀疏得多，但它具有相当的地标点标记精度。其次，它可以在消费级个人计算机上高效运行。最后，它具有通用性，可在种内水平上应用于任何感兴趣的生物结构，无论是否有解剖图谱。我们的验证程序表明，该方法能够恢复与手动数字化获得的形态变异模式在很大程度上相当的种内模式，这表明使用自动地标点标记方法不应导致关于形态变异多变量模式性质的不同结论。所提出的基于点云的方法有可能提高形态测量学研究的规模和可重复性。为了让没有编程经验的用户能够开箱即用ALPACA，我们将其实现为SlicerMorph模块。SlicerMorph是一个扩展，可在开源的3D Slicer生物医学可视化生态系统中实现几何形态测量数据收集和3D标本分析。我们预计，方便地访问这个平台将使ALPACA在生态学和进化领域得到广泛应用。
