Decoding Anadara shell morphology with deep learning

Abstract

Accurate shell shape quantification is critical for studying biodiversity and evolution, yet intraspecific variability in bivalves makes morphology-based identification difficult. Traditional methods, including landmark-based analyses and elliptic Fourier descriptors, suffer either from subjectivity in homologous point selection or from limited use of contour information. Here, we introduce Morpho-VAE, a deep generative framework integrating a variational autoencoder with a supervised classifier, to analyze shell images of five Anadara species. Morpho-VAE outperforms conventional approaches in species classification by embedding morphological variation into a low-dimensional space where species cluster distinctly. To highlight species-specific morphological patterns, we develop a patch masking assay, revealing the hinge line as a shared morphological marker across species and species-specific regions near the umbo and anterior ventral margin. The decoder further enables morphological visualization via image reconstruction and interpolation. Our results show that Morpho-VAE can automatically extract species-defining morphological patterns from raw images, providing complementary or novel insights beyond traditional morphometric methods.