Scientists have developed an AI-based technique that vastly speeds up the analysis of fossils.
Espen Knutsen is a Senior Lecturer with James Cook University and Senior Scientist/Curator of Palaeontology at the Queensland Museum, Tropics. Along with JCU Deep Learning expert Senior Lecturer Dmitry Konovalov he has been working on how to more rapidly analyse fossils encased in rock.
"Computed Tomography (CT) scanning provides palaeontologists with a way to look inside bone and study fragile fossil material without the need for physically removing the surrounding rock," said Dr Knutsen.
He said the CT datasets consist of a stack of x-ray image slices which are imported into a computer - but it needs to be manually told what parts of each slice is fossil and what is rock before it can produce a 3D model.
"With ever-improving equipment, the size of datasets and image resolution have significantly increased, which means a significant amount of time invested in manually segmenting data.
"With datasets often exceeding 2000 images per sample, this process can take months to complete," said Dr Knutsen.
Instead, the scientists manually segmented around 2% of 2000 image slices and used these to train a Deep Learning model which completed the task by itself.
"We achieved a highly precise 3D representation of a tiny Triassic reptile from Queensland that was around 240 million-years-old. This was achieved in days rather than months," said Dr Knutsen.
The researchers will now work to expand the capability of their Deep Learning model to be used on other and more diverse fossil material.
The paper Accelerating segmentation of fossil CT scans through Deep Learning was published in the journal Scientific Reports – available here.
