Foram3D: A pipeline for 3D synthetic data generation and rendering of foraminifera for image analysis and reconstruction

Edgar Lobaton, Sanjana Banerjee, Turner Richmond, Thomas Marchitto, Michael Daniele

Abstract:

Foraminifera play an important role in oceanographic and paleoceanographic research. The test morphology
and chemistry within species, as well as the presence or absence of certain species, are affected by
environmental conditions. Classification of different species of foraminifera is a crucial yet tedious task for
researchers. Deep-learning approaches can help with morphological studies and aid in species classification;
however, they require large-scale datasets that are challenging to obtain and annotate because of the extremely
small size and delicate handling of these microorganisms. In this work, we expand on an existing mathematical
model for foraminifera shell growth to generate 3D synthetic models to aid in these studies. We define
parameter spaces for the model which are intended to approximate seven randomly chosen foraminifera taxa.
Along with providing an open-source code base to support other researchers in generating models and studying
growth patterns, we further extend the synthetic data generation to include a rendering component that mimics
two existing robotic imaging systems. We provide two use cases for our synthetic dataset. First, we show
how orientation can affect the automated classification of different species and how incorporating aleatoric
uncertainty indicators can help select the next views of the samples to significantly improve classification
accuracy from 82% to 89%. Next, we show how a sparse set of synthetic 2D images can be used to extract
3D morphology of foraminifera using Neural Radiance Fields (NeRFs).