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Scientists at Queensland Museum and James Cook University have developed an AI-based technique that dramatically accelerates fossil analysis. This innovative method reduces the time required for analyzing fossil CT scans from months to just days. The research, published in Scientific Reports, showcases the potential of AI in paleontology by enabling rapid and precise 3D modeling of ancient specimens.
The technique involves training a Deep Learning model on a small portion of CT scan data, allowing it to autonomously segment fossil images. This breakthrough not only streamlines the analysis process but also enhances the ability to generate more data from fossil collections. The researchers aim to expand this AI model's capabilities to analyze a wider variety of fossil materials, further advancing the field.
• AI reduces fossil analysis time from months to days.
• Deep Learning model autonomously segments fossil CT scan data.
In this context, Deep Learning is utilized to automate the segmentation of fossil CT scans, significantly speeding up the analysis process.
The article discusses how CT scanning allows paleontologists to examine fossils without damaging them.
In this study, segmentation is crucial for distinguishing fossil material from surrounding rock in CT scans.
The Queensland Museum is involved in pioneering research that integrates AI into paleontological studies.
James Cook University collaborates with the Queensland Museum to advance the application of AI in fossil analysis.
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