2025-07-29 ノースカロライナ州立大学
Image credit: Sanjana Banerjee.
<関連情報>
- https://news.ncsu.edu/2025/07/tiny-fossils-smarter-robots/
- https://www.sciencedirect.com/science/article/pii/S0377839825000519?via%3Dihub
Foram3D: 画像分析と再構築のための有孔虫の3D合成データ生成とレンダリングのためのパイプライン Foram3D: A pipeline for 3D synthetic data generation and rendering of foraminifera for image analysis and reconstruction
Sanjana Banerjee, Turner Richmond, Thomas Marchitto, Michael Daniele, Edgar Lobaton
Marine Micropaleontology Available online: 11 July 2025
DOI:https://doi.org/10.1016/j.marmicro.2025.102486
Highlights
- A model for generating synthetic foraminifera data is proposed in this work
- Computer graphics software provides realistic rendering capabilities
- The synthetic model provides a large dataset to test classification algorithms .
- Impact of viewing-angle on the classification of foraminifera is studied
- 3D foram morphology can be extracted from a sparse set of synthetic 2D images
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).
