ArticleScientific reports2024
Accelerating segmentation of fossil CT scans through Deep Learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Opening the black box: explainable AI for automated bioturbation analysis in cores and outcrops.Scientific reports · 2026Article
- The oldest sepioid cephalopod from the Cretaceous discovered by Digital fossil-mining with zero-shot learning AI.Communications biology · 2026Article
- Photon-Counting Detector CT Scan of Dinosaur Fossils: Initial Experience.Journal of imaging · 2025Article
- Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil.Scientific reports · 2025Article
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Authors and funding
2 authors.
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Abstract
Recent developments in Deep Learning have opened the possibility for automated segmentation of large and highly detailed CT scan datasets of fossil material. However, previous methodologies have required large amounts of training data to reliably extract complex skeletal structures. Here we present a method for automated Deep Learning segmentation to obtain high-fidelity 3D models of fossils digitally extracted from the surrounding rock, training the model with less than 1%-2% of the total CT dataset. This workflow has the capacity to revolutionise the use of Deep Learning to significantly reduce the processing time of such data and boost the availability of segmented CT-scanned fossil material for future research outputs. Our final Unet segmentation model achieved a validation Dice similarity of 0.96.
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