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Jeholosaurus femur biomechanics from finite element analysis (FEA) (pdf),
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Ben Creisler
Apr 22, 2025, 1:28:58 PMApr 22
to DinosaurMa...@googlegroups.com
Ben Creisler
A new paper:
Lijia Zhang, Zixiong Cao & Qi Zhao (2025)
Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil
Scientific Reports 15: 13964
doi: https://doi.org/10.1038/s41598-025-99131-4
https://www.nature.com/articles/s41598-025-99131-4
Finite element analysis (FEA), a biomechanical simulation technique capable of providing direct mechanical visualization for CT-based digital models, has been extensively applied to fossil image datasets to address key evolutionary questions in paleontology. However, the rock matrix filling intertrabecular space of fossils often causes severe deviations in FEA results. Segmentation strategies such as thresholding and manual labeling have been employed to mitigate these disturbances. However, the efficiency of manual segmentation and the accuracy of thresholding remain questionable. In this study, we applied FEA combined with deep learning-based segregation on a femoral specimen of Jeholosaurus (a small bipedal dinosaur). This novel methodology efficiently generates the FE model with stress distribution that closely reflects the trabecular architecture in fossils of extinct taxa, reflecting a more natural state of biomechanical performance with high biological reality. Our approach provides a practical strategy for studying the biomechanics, functional morphology, and taxonomy of extinct species.
Free pdf:
Lijia Zhang, Zixiong Cao & Qi Zhao (2025)
Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil
Scientific Reports 15: 13964
doi: https://doi.org/10.1038/s41598-025-99131-4
https://www.nature.com/articles/s41598-025-99131-4
Finite element analysis (FEA), a biomechanical simulation technique capable of providing direct mechanical visualization for CT-based digital models, has been extensively applied to fossil image datasets to address key evolutionary questions in paleontology. However, the rock matrix filling intertrabecular space of fossils often causes severe deviations in FEA results. Segmentation strategies such as thresholding and manual labeling have been employed to mitigate these disturbances. However, the efficiency of manual segmentation and the accuracy of thresholding remain questionable. In this study, we applied FEA combined with deep learning-based segregation on a femoral specimen of Jeholosaurus (a small bipedal dinosaur). This novel methodology efficiently generates the FE model with stress distribution that closely reflects the trabecular architecture in fossils of extinct taxa, reflecting a more natural state of biomechanical performance with high biological reality. Our approach provides a practical strategy for studying the biomechanics, functional morphology, and taxonomy of extinct species.
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