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Research project

Machine Learning to Aggregate X-Ray Computed Tomographic Trait Data

Project overview

The environmental sciences are undergoing an accelerating data revolution. The excitement at the capacity for next-generation sequencing, transcriptomic profiling and remote sensing of our environment has led to an unparalleled expansion in the types and amount of data available. X-ray computed tomography (x-ray CT) has established itself as a standard tool for extracting Synchrotron-equivalent resolution of three-dimensional ecological trait data. Within the discipline of environmental science, these images are extracted in an ad hoc basis across different laboratories. The discipline hop needed is generalisable pre-filtering software that can extract comparable metrics regardless of the original scanning protocols. Removing such measurement bias would open up appropriate comparative analysis on rapidly developing tools.


Lead researcher

Professor Tom Ezard

Professor of Evolutionary Ecology

Research interests

  • The bridge from micro- to macroevolution
  • Scaling ecological dynamics across individuals, populations and ecosystems
  • Demography for conservation and life history evolution
Connect with Tom
Other researchers

Dr Xiaohao Cai

Lecturer in Computer Science

Research interests

  • Image/signal/data processing
  • Computer vision
  • Machine learning
Connect with Xiaohao

Collaborating research institutes, centres and groups

Research outputs