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.

几何形态测量学（geometric morphometrics）方法目前已被广泛应用于众多生物科学领域的形态量化研究。但此类方法在大型数据集上的应用仍受限于手动地标点放置操作：该操作不仅会限制可设置的地标点数量，还会引入观察者偏差。为推动该领域发展，亟需实现形态表型分析的自动化，以在最小化观察者偏差的前提下，全面捕捉形态变异的完整特征。为此，我们提出**形态变异量化器（Morphological Variation Quantifier，简称morphVQ）**，这是一款面向功能域内形状变异的量化、分析与探索的形状分析流程工具。morphVQ通过描述符学习对完整三角网格模型之间的功能对应关系进行估计，以此替代传统的地标点配置方案。借助数据集内各标本对之间的功能映射关系，即可开展形状变异的分析与探索工作。morphVQ采用**一致性缩放优化（Consistent ZoomOut）**算法对上述功能映射进行优化，进而生成全新的形状变异表征，以及基于面积与共形（角度）的潜在形状空间差异（latent shape space differences，简称LSSDs）。我们将该新型形状变异表征与通过手动数字化及现有自动化形态表型分析工具auto3DGM所获得的形状变量进行了对比。实验结果表明，LSSDs的表现优于现代3DGM与auto3DGM，且具备更高的计算效率。通过对完整表面进行特征表征，本方法可在形状分析中纳入更多形态学细节。我们可基于该方法对已知的生物类群（如属级分类归属）进行分类，且分类精度可与现有方法媲美。本方法生成的形状空间与现代3DGM及auto3DGM所生成的形状空间高度相似，而基于LSSDs推导得到的区分度函数则可揭示不同类群间的形状变异差异。morphVQ可实现形状的自动化捕获，同时规避手动数字化地标点带来的诸多局限，因此为几何形态测量学工具集增添了一款新颖且高效的计算型工具。