Automated segmentation of synchrotron-scanned fossils

Computed tomography has revolutionised the study of the internal three-dimensional structure of fossils. Historically, fossils typically spent years in preparation to be freed from the enclosing rock. Now, X-ray and synchrotron tomography reveal structures that are otherwise invisible, and data acquisition can be fast. However, manual segmentation of these 3D volumes can still take months to years. This is especially challenging for resource-poor teams, as scanning may be free, but the computing power and (AI-assisted) segmentation software required to handle the resulting large data sets are complex to use and expensive. Here we present a free, browser-based segmentation tool that reduces computational overhead by splitting volumes into small chunks, allowing processing on low-memory, inexpensive hardware. Our tool also speeds up collaborative ground-truth generation and 3D visualisation, all in-browser. We developed and evaluated our pipeline on various open-data scans of differing contrast, resolution, textural complexity, and size. Our tool successfully isolated the Thrinaxodon and Broomistega pair from an Early Triassic burrow. It isolated cranial bones from the Cretaceous acipenseriform Parapsephurus willybemisi on both 45.53 µm and 13.67 µm resolution (voxel size) scanning data. We also isolated bones of the Middle Triassic sauropterygian Nothosaurus and a challenging scan of a squamate embryo inside an egg dating back to the Early Cretaceous. Our tool reliably reproduces expert-supervised segmentation at a fraction of the time and cost, offering greater accessibility than existing tools. Beyond the online tool, all our code is open source, enabling contributions from the palaeontology community to further this emerging machine-learning ecosystem.

Authors

Institutions

Publication Details

Journal
Fossil record
Published
2025-03-06
DOI
https://doi.org/10.3897/fr.28.e139379
Primary Topic
Advanced X-ray Imaging Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated segmentation of synchrotron-scanned fossils

Fredrik K. Gustafsson, Donglei Chen, Jordan Matelsky, Brock A. Wester et al.
Fossil record
Advanced X-ray Imaging Techniques
article

Automated segmentation of synchrotron-scanned fossils

Fredrik K. Gustafsson, Donglei Chen, Jordan Matelsky, Brock A. Wester, Melanie A. D. During, Konrad P. Körding, Per Ahlberg, Dennis F. A. E. Voeten, Thomas B. Schön
article en

Abstract

Computed tomography has revolutionised the study of the internal three-dimensional structure of fossils. Historically, fossils typically spent years in preparation to be freed from the enclosing rock. Now, X-ray and synchrotron tomography reveal structures that are otherwise invisible, and data acquisition can be fast. However, manual segmentation of these 3D volumes can still take months to years. This is especially challenging for resource-poor teams, as scanning may be free, but the computing power and (AI-assisted) segmentation software required to handle the resulting large data sets are complex to use and expensive. Here we present a free, browser-based segmentation tool that reduces computational overhead by splitting volumes into small chunks, allowing processing on low-memory, inexpensive hardware. Our tool also speeds up collaborative ground-truth generation and 3D visualisation, all in-browser. We developed and evaluated our pipeline on various open-data scans of differing contrast, resolution, textural complexity, and size. Our tool successfully isolated the Thrinaxodon and Broomistega pair from an Early Triassic burrow. It isolated cranial bones from the Cretaceous acipenseriform Parapsephurus willybemisi on both 45.53 µm and 13.67 µm resolution (voxel size) scanning data. We also isolated bones of the Middle Triassic sauropterygian Nothosaurus and a challenging scan of a squamate embryo inside an egg dating back to the Early Cretaceous. Our tool reliably reproduces expert-supervised segmentation at a fraction of the time and cost, offering greater accessibility than existing tools. Beyond the online tool, all our code is open source, enabling contributions from the palaeontology community to further this emerging machine-learning ecosystem.

Fossil recordVol. 28(1)
Uppsala University (SE), Fryske Akademy (NL), Johns Hopkins University Applied Physics Laboratory (US), Karolinska Institutet (SE), University of Pennsylvania (US)
University of Pennsylvania, Johns Hopkins University, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Karolinska Institutet, Vetenskapsrådet, Uppsala Universitet
Openalex Percentile: Top 95%
Advanced X-ray Imaging Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.