PyTorch geometric datasets for morphVQ models

The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset, we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation and area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit. Methods The main dataset consists of 102 triangular meshes from laser surface scans of hominoid cuboid bones. These cuboids were from wild-collected individuals housed in the American Museum of Natural History, the National Museum of Natural History, the Harvard Museum of Comparative Biology, and the Field Museum. Hylobates, Pongo, Gorilla, Pan, and Homo are all well represented. Each triangular mesh is denoised, remeshed, and cleaned using the Geomagic Studio Wrap Software. The resulting meshes vary in vertex-count/resolution from 2,000 - 390,000. Each mesh is then upsampled or decimated to an even 12,000 vertices using the recursive subdivisions process and quadric decimation algorithm implemented in VTK python. The first of the two smaller datasets is comprised of 26 hominoid medial cuneiforms meshes isolated from laser surface scans obtained from the same museum collections listed above. The second dataset comprises 33 mouse humeri meshes from micro-CT data (34.5 μm resolution using a Skyscan 1172). These datasets were processed identically to the 102 hominoid cuboid meshes introduced above.几何形态测量学（geometric morphometrics）方法目前被广泛应用于众多生物科学领域的形态量化研究。然而，此类方法在大型数据集上的应用却受到诸多限制：手动地标点放置不仅会限制地标点的数量，还会引入观察者偏差。为推动该领域发展，我们亟需实现形态表型的自动化，以最小化观察者偏差的方式捕获形态变异的全面表征。在此，我们提出"形态变异量化器（Morphological Variation Quantifier, morphVQ）"——一款用于功能域内形态变异量化、分析与探索的形状分析流程。morphVQ通过描述符学习，以整体三角网格（triangular meshes）的功能对应关系替代地标点配置，实现形态分析。借助数据集内标本对之间的功能映射，我们可对形态变异进行分析与探索。morphVQ采用一致性放大细化（Consistent ZoomOut refinement）优化上述功能映射，并生成全新的形态变异表征，以及基于面积与共形（角度）的潜形状空间差异（latent shape space differences, LSSDs）。我们将该新型形态变异表征与手动数字化及现有自动化形态表型方法auto3DGM所获得的形状变量进行对比，结果表明，LSSDs不仅性能优于现代3DGM与auto3DGM，还具备更高的计算效率。通过对整体表面进行表征，我们的方法可在形状分析中纳入更多形态细节。我们可对已知的生物学类群进行分类，例如以相当的准确率区分属级归属。本方法生成的形状空间与现代3DGM及auto3DGM的结果具有相似性，而基于LSSDs得到的差异性函数则可揭示不同类群间的形态变异差异。morphVQ可实现形态的自动化捕获，同时规避手动数字化地标点的局限性，因此为几何形态测量学工具包增添了一种新颖且高效的计算方法。 方法 主数据集包含102个类人猿骰骨（hominoid cuboid bones）的激光表面扫描三角网格。这些骰骨采集自野外个体，馆藏于美国自然历史博物馆、国家自然历史博物馆、哈佛大学比较生物学博物馆以及菲尔德自然历史博物馆。样本涵盖长臂猿属（Hylobates）、猩猩属（Pongo）、大猩猩属（Gorilla）、黑猩猩属（Pan）与人属（Homo）。 所有三角网格均通过Geomagic Studio Wrap软件进行去噪、重网格化与清理操作。处理后的网格顶点数/分辨率范围为2000至390000。随后，借助VTK Python库中实现的递归细分算法与二次降采样算法，将每个网格统一重采样至12000个顶点。 两个小型子数据集的第一个包含26个类人猿内侧楔骨（medial cuneiforms）三角网格，这些网格同样源自上述博物馆馆藏的激光表面扫描数据。第二个数据集包含33个小鼠肱骨（mouse humeri）三角网格，数据来源于显微计算机断层扫描（micro-CT，分辨率34.5μm，使用Skyscan 1172设备采集）。这两个子数据集的处理流程与前述102个类人猿骰骨网格完全一致。