Biomedical
Peer Reviewed
Doi: http://dx.doi.org/10.1038/s41598-024-71245-1
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.
Show by month | Manuscript | Video Summary |
---|---|---|
2024 December | 26 | 26 |
2024 November | 45 | 45 |
2024 October | 16 | 16 |
Total | 87 | 87 |
Show by month | Manuscript | Video Summary |
---|---|---|
2024 December | 26 | 26 |
2024 November | 45 | 45 |
2024 October | 16 | 16 |
Total | 87 | 87 |
Doi: http://dx.doi.org/10.1038/s41598-024-71245-1