Ana Namburete

About

My research career started in Vancouver, Canada at the Neuromuscular Mechanics Laboratory at Simon Fraser University. I investigated non-uniform muscle fibre architecture and its effect on whole muscle performance using ultrasound image analysis. I then received a Commonwealth Scholarship to study fetal brain development in my PhD with Prof. Alison Noble as my supervisor at the University of Oxford. I design machine learning algorithms to link the sonogrographic image patterns to the process of brain maturation. I became a Principal Investigator at Oxford's Department of Engineering Science, where I developed a pipeline to process ultrasound scans of the fetal brain. In 2016, I became an independent group leader at the Institute of Biomedical Engineering, after being awarded the prestigious Royal Academy of Engineering Research Fellowship. I was also appointed as a Junior Research Fellow in Engineering Science at St Hilda's College. In 2021, I was appointed as a Research Lecturer in AI and Health Data Science at the Department of Computer Science and Tutorial Fellow in Computer Science at Pembroke College.

Employment

• University of Oxford Research Lecturer in AI and Health Data Science
  2021 - Present
• University of Oxford RAEng Research Fellow (Academic Faculty)
  2015 - 2021

Education

• University of Oxford DPhil
  2011 - 2014
• Simon Fraser University BASc
  2006 - 2011

Projects & Funding

• Ultrasound-Based Assessment of Brain Folding Patterns in Early Pregnancy Academy of Medical Sciences
  2021 - 2023
• Deep Learning Models for Fetal Monitoring and Decision Support in Labour Engineering and Physical Sciences Research Council
  2021 - 2024
• Determining cerebrovascular reactivity from the pupil flash response Engineering and Physical Sciences Research Council
  2019 - 2024
• Computer Aided Diagnosis for the Analysis of the Fetal Brain and Heart on Ultrasound Engineering and Physical Sciences Research Council
  2019 - 2022
• To automatically estimate gestational age at all stages of pregnancy from a routine ultrasound image without the need for a sonographer by developing a computational tool called Autodate that identifies physical features of the fetal brain Bill & Melinda Gates Foundation
  2015 - 2017

Publications (54)

• Semi-supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model
  2026 DOI: 10.1007/978-3-032-09513-8_6
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• Is Your Style Transfer Doing Anything Useful? An Investigation Into Hippocampus Segmentation and the Role of Preprocessing
  2024 DOI: 10.1101/2024.08.22.24312425
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• Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years
  Ana I. L. Namburete, Bartłomiej W. Papież, Michelle Fernandes +25 more
  Nature 2023 DOI: 10.1038/s41586-023-06630-3
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• The impact of projected increases in obesity prevalence on incident liver disease in the UK: Insights from Bayesian-network modelling
  2023 DOI: 10.1145/3608298.3608307
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• Challenges for machine learning in clinical translation of big data imaging studies
  Nicola K. Dinsdale, Emma Bluemke, Vaanathi Sundaresan

  +3 more

  • Mark Jenkinson
  • Stephen M. Smith
  • Ana I. L. Namburete
  Neuron 2022 DOI: 10.1016/j.neuron.2022.09.012
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• Assessment of Regional Cortical Development Through Fissure Based Gestational Age Estimation in 3D Fetal Ultrasound
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021 DOI: 10.1007/978-3-030-87735-4_23
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• Challenges for machine learning in clinical translation of big data imaging studies
  arXiv 2021
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• Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal
  NeuroImage 2021 DOI: 10.1016/j.neuroimage.2020.117689
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• ImplicitVol: Sensorless 3D ultrasound reconstruction with deep implicit representation
  arXiv 2021
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• Learning patterns of the ageing brain in MRI using deep convolutional networks
  NeuroImage 2021 DOI: 10.1016/j.neuroimage.2020.117401
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• Learning to map 2D ultrasound images into 3D space with minimal human annotation
  Medical Image Analysis 2021 DOI: 10.1016/j.media.2021.101998
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• Preface
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021
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• STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation
  bioRxiv 2021 DOI: 10.1101/2021.11.26.470124
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• Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learning
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021 DOI: 10.1007/978-3-030-87196-3_7
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• Sli2Vol: Annotate a 3D volume from a single slice with self-supervised learning
  arXiv 2021
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• Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning
  bioRxiv 2021 DOI: 10.1101/2021.09.29.462430
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• TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021 DOI: 10.1007/978-3-030-87193-2_24
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• TEDS-net: Enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations
  arXiv 2021
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• The association between flow and oxygenation and cortical development in fetuses with congenital heart defects using a brain-age prediction algorithm
  Prenatal Diagnosis 2021 DOI: 10.1002/pd.5813
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• Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
  IEEE Transactions on Medical Imaging 2020 DOI: 10.1109/TMI.2020.3018560
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