ForametCeTera, a novel CT scan dataset to expedite classification research of (non-)foraminifera

• Published in: Scientific data Vol. 11; no. 1; pp. 642 - 7
• Main Authors: Luijmes, Joost, van Leeuwen, Tristan, Renema, Willem
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.06.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/158/2165; 631/158/2446/2447; 631/158/670; 631/1647/48; 704/829/826; Automation; Classification; Climate change; Computed tomography; Coral Reefs; Core analysis; Cores; Data Descriptor; Datasets; Environmental changes; Foraminifera - classification; Geologic Sediments; Humanities and Social Sciences; Medical imaging; multidisciplinary; Science; Science (multidisciplinary); Tomography, X-Ray Computed
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-024-03476-w

This paper introduces ForametCeTera, a pioneering dataset designed to address the challenges associated with automating the analysis of benthic foraminifera in sediment cores. Foraminifera are sensitive sentinels of environmental change and are a crucial component of carbonate-denominated ecosystems, such as coral reefs. Studying their prevalence and characteristics is imperative in understanding climate change. However, analysis of foraminifera contained in core samples currently requires washing, sieving and manual quantification. These methods are thus time-consuming and require trained experts. To overcome these limitations, we propose an alternative workflow utilizing 3D X-ray computational tomography (CT) for fully automated analysis, saving time and resources. Despite recent advancements in automation, a crucial lack of methods persists for segmenting and classifying individual foraminifera from 3D scans. In response, we present ForametCeTera, a diverse dataset featuring 436 3D CT scans of individual foraminifera and non-foraminiferan material following a high-throughput scanning workflow. ForametCeTera serves as a foundational resource for generating synthetic digital core samples, facilitating the development of segmentation and classification methods of entire core sample CT scans.