Georgia Kenyon

Georgia Kenyon

Higher Degree by Research Candidate

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

I am a Neuroimaging PhD student (3rd year), enrolled on the International Doctoral Training Partnership between the University of Nottingham's Precision Imaging Beacon (School of Medicine), and The University of Adelaide's Australian Institute of Machine Learning (AIML) (School of Computer Science). I am currently based in Australia.

My interests lie in Computer Vision, Machine Learning, and Medical Image Analysis. I am currently developing 'Anatomically-based Deep Learning Models for Improved Neuroimaging Analysis'.

I graduated as a First-Class Honours Masters student in 'Mechanical Engineering with Biomechanics' at The University of Sheffield in 2020, and was subsequently awarded a Joint Award research scholarship by The University of Adelaide to conduct my current PhD.

I’m on a joint PhD between the University of Nottingham (UK) and the University of Adelaide (Australia), and I am working on using AI, specifically deep learning, to analyse brain MRI scans.

My project focuses on combining the knowledge of human anatomy with deep learning models, to improve the segmentation of the brain into its multiple structures. I am particularly focused on extracting the blood vessels from the brain, which are often ignored. Understanding where vessels are in the brain can help to identify anomalies like cerebral microbleeds, and benefit the analysis of blood flow in imaging types such as Arterial Spin Labelling (ASL). 

  • Journals
    Year Citation
    2025 Kirk, T. F., Kenyon, G. G., Craig, M. S., & Chappell, M. A. (2025). Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI. Frontiers in Neuroscience, 19, 10 pages.
    DOI
    2024 Rippa, M., Schulze, R., Kenyon, G., Himstedt, M., Kwiatkowski, M., Grobholz, R., . . . Burn, F. (2024). Evaluation of Machine Learning Classification Models for False-Positive Reduction in Prostate Cancer Detection Using MRI Data. Diagnostics, 14(15), 14 pages.
    DOI Scopus3
    2023 Kenyon, G., Lau, S., Chappell, M. A., & Jenkinson, M. (2023). Segmentation method for cerebral blood vessels from MRA using hysteresis.

  • Conference Papers
    Year Citation
    - Medical Image Understanding and Analysis (n.d.). In Medical Image Understanding and Analysis. Frontiers Media SA.
    DOI

  • Conference Items
    Year Citation
    2023 Kenyon, G., Lau, S., Chappell, M., & Jenkinson, M. (2023). Open Access Automated Segmentation of Cerebral Blood Vessels from MRA using Hysteresis. Poster session presented at the meeting of INTERNATIONAL JOURNAL OF STROKE. SAGE PUBLICATIONS LTD.

University Roles:

- AIML Student Ambassador, representing AIML at events such as: Student Recruitment, AIML Tours and Presentations, Offsite events, Ingenuity Open Day and AIML Open Days.

- School of Computer Science HDR Student Representative: Volunteer representative to ensure that all HDR students within the faculty have a direct point of contact for any issues that may be faced, and to be a spokesperson regarding issues or changes that may need to be relayed to the rest of the faculty and Head of School.

  • Community Engagement
    Date Title Engagement Type Institution Country
    2023 - ongoing Magazine Interview in Lot Fourteen’s ‘Boundless’ Scientific Community Engagement Lot Fourteen Australia
Connect With Me
External Profiles