How many specimens make a sufficient training set for automated three dimensional feature extraction?

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 ..., Data collection
50 planktonic foraminifera, comprising 4 Menardella menardii, 17 Menardella limbata, 18 Menardella exilis, and 11 Menardella pertenuis specimens, were used in our analyses (electronic supplementary material, figures S1 and S2). The taxonomic classification of these species was established based on the analysis of morphological characteristics observed in their shells. In this context, all species are characterised by lenticular, low trochosprial tests with a prominent keel [13]. Discrimination among these species is achievable, as M. limbata can be distinguished from its ancestor, M. menardii, by having a greater number of chambers and a smaller umbilicus. Moreover, M. exilis and M. pertenuis can be discerned from M. limbata by their thinner, more polished tests and reduced trochospirality. Furthermore, M. pertenuis is identifiable by a thin plate extending over the umbilicus and possessing a greater number of chambers in the final whorl compared to M. exilis [13].
The s..., , # Data from: How many specimens make a sufficient training set for automated three dimensional feature extraction?

[https://doi.org/10.5061/dryad.1rn8pk12f](https://doi.org/10.5061/dryad.1rn8pk12f)

All computer code and final raw data used for this research work are stored in GitHub: [https://github.com/JamesMulqueeney/Automated-3D-Feature-Extraction](https://github.com/JamesMulqueeney/Automated-3D-Feature-Extraction) and have been archived within the Zenodo repository: [https://doi.org/10.5281/zenodo.11109348. ](https://doi.org/10.5281/zenodo.11109348)

This data is the additional primary data used in each analysis. These include: CT Image Files, Manual Segmentation Files (use for training or analysis), Inputs and Outputs for Shape Analysis and an example .h5 file which can be used to practice AI segmentation. 

## Description of the data and file structure

The primary data is arranged into the following:

1. **Image_Files.zip:** Foraminiferal CT data used in the analysis. 
2. **I...