How many specimens make a sufficient training set for automated 3D feature extraction?

Abstract

Deep learning has emerged as a robust tool for automating feature extraction from 3D images, offering an efficient alternative to labour-intensive and potentially biased manual image segmentation methods. However, there has been limited exploration into the optimal training set sizes, including assessing whether artificial expansion by data augmentation can achieve consistent results in less time and how consistent these benefits are across different types of traits. In this study, we manually segmented 50 planktonic foraminifera specimens from the genus Menardella to determine the minimum number of training images required to produce accurate volumetric and shape data from internal and external structures. The results reveal unsurprisingly that deep learning models improve with a larger number of training images with eight specimens being required to achieve 95% accuracy. Furthermore, data augmentation can enhance network accuracy by up to 8.0%. Notably, predicting both volumetric and shape measurements for the internal structure poses a greater challenge compared to the external structure, due to low contrast between different materials and increased geometric complexity. These results provide novel insight into optimal training set sizes for precise image segmentation of diverse traits and highlight the potential of data augmentation for enhancing multivariate feature extraction from 3D images.

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Identifiers

ORCID for James M. Mulqueeney: orcid.org/0000-0003-3502-745X

ORCID for Alex Searle-Barnes: orcid.org/0000-0003-0389-7717

ORCID for Anieke Brombacher: orcid.org/0000-0003-2310-047X

ORCID for Thomas H.G. Ezard: orcid.org/0000-0001-8305-6605

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