Biology-guided machine learning for robust anatomical correspondence across biodiversity datasets

Three-dimensional imaging is rapidly increasing the scale of biological morphometric datasets, but establishing automated anatomical correspondence remains a major bottleneck. Conventional point-cloud registration methods transfer landmarks efficiently, yet poor initialization or high morphological variation can produce geometrically plausible but biologically implausible deformations. Here, we developed a statistical-shape-model (SSM)-regularized coherent point drift (CPD) estimator for automated morphometric landmark transfer. It replaces the unconstrained non-rigid CPD update with optimization in SSM coefficient space and regularizes coefficients by inverse population covariance, so deformations along low-variance biological modes are penalized more strongly. We compared pose-matched conventional CPD, SSM-CPD, and SSM-CPD with refinement in leave-one-out atlas experiments on laboratory mouse skulls (N=249), primate molars (N=115), and squamate vertebrae (N=254) under aligned, mildly perturbed, and severely perturbed initialization. We evaluated these formulations in terms of landmark accuracy, failure-tail behavior, runtime, and preservation of morphometric structure. Under severe perturbation, we also evaluated an integrated recovery procedure that optimizes the starting template, recovers global pose, and then applies SSM-CPD with refinement. Severe misalignment produced widespread failures in pose-matched conventional CPD, whereas SSM regularization reduced median error from 13 - 21% to approximately 1 - 3% of the target bounding-box diagonal. This improvement was highly consistent for skulls and molars. In the morphologically complex vertebral dataset, however, low median error concealed a substantial minority of catastrophic failures (28% of vertebrae above a 6% error threshold), which integrated recovery reduced to 4%. Across datasets, automated landmarks preserved the major axes and pairwise relationships of morphometric shape space, and local CPD refinement improved covariance preservation. Embedding learned biological variation directly into registration broadens the range of starting conditions and anatomies for which automated landmark transfer remains reliable. Our results also show that failure tails and downstream morphometric fidelity must be assessed alongside median accuracy when automated landmarking methods are evaluated.