High-throughput micro-CT scanning and deep learning segmentation workflow for analyses of shelly invertebrates and their fossils: Examples from marine Bivalvia

Abstract

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The largest source of empirical data on the history of life largely derives from the marine invertebrates. Their rich fossil record is an important testing ground for macroecological and macroevolutionary theory, but much of this historical biodiversity remains locked away in consolidated sediments. Manually preparing invertebrate fossils out of their matrix can require weeks to months of careful excavation and cannot guarantee the recovery of important features on specimens. Micro-CT is greatly improving our access to the morphologies of these fossils, but it remains difficult to digitally separate specimens from sediments of similar compositions, e.g., calcareous shells in a carbonate rich matrix. Here we provide a workflow for using deep learning—a subset of machine learning based on artificial neural networks—to augment the segmentation of these difficult fossils. We also provide a guide for bulk scanning fossil and Recent shells, with sizes ranging from 1 mm to 20 cm, enabling the rapid acquisition of large-scale 3D datasets for macroevolutionary and macroecological analyses (300–500 shells in 8 hours of scanning). We then illustrate how these approaches have been used to access new dimensions of morphology, allowing rigorous statistical testing of spatial and temporal patterns in morphological evolution, which open novel research directions in the history of life.

Keywords

• paleontology
• bivalve
• 3D morphometrics
• high-throughput morphometry
• deep learning
• computed tomography