Dr Ana Namburete

BASc (Simon Fraser University), DPhil (Oxon)


Associate Research Fellow in Engineering

Dr Namburete graduated from Simon Fraser University with a First Class Honours degree in Biomedical Engineering. As a holder of the Commonwealth Scholarship, she joined the Biomedical Image Analysis Lab at Oxford in 2011 where she completed a DPhil in Engineering Science. Dr Namburete has been awarded a prestigious five-year Royal Academy of Engineering Fellowship.


As a biomedical engineer, Dr Namburete's work comprises collaborations with medical professionals to identify problems relevant to prenatal care in low-income settings, and designing the appropriate engineering solutions. Specifically, her interests lie in developing data analytics and machine learning tools to enhance the diagnostic value of ultrasound image data.

Recent work involving collaborations with clinicians at the John Radcliffe Hospital at Oxford and in Kilifi, Kenya has been awarded a Grand Challenge Explorations Grant (Round 14: Exploring New Ways to Measure Brain Development and Gestational Age) from the Bill and Melinda Gates Foundation. She now serves as a Principal Investigator on that project. Her postdoctoral research is geared towards developing a computational tool that identifies physical features of the fetal brain from a routine ultrasound image to automatically, and more accurately, estimate gestational age at any stage of pregnancy. This tool is designed for use in resource-constrained clinics.


Journal Articles

 Namburete, A. I. L., Stebbing, R. V., Yaqub, M., Kemp, B., Papageorghiou, A. T., Noble, J. A., “Learning-based prediction of gestational age from ultrasound images of the fetal brain”, Medical Image Analysis, 21 (1), 72-86, 2015

 Stebbing, R. V., Namburete, A. I. L., Upton, R., Leeson, P., Noble, J. A., “Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography”, Medical Image Analysis, 21 (1), 29-39, 2015

 Namburete, A. I. L., Noble, J. A., “Nakagami-based AdaBoost learning framework for detection of anatomical landmarks in 2D fetal neurosonograms”, Special Issue of the British Machine Vision Association (BMVA), 2, 1-16, 2013 (Invited contribution)

 Namburete, A. I. L., Wakeling, J. M., “Regional variations in fascicle curvature within a muscle belly change during contraction”, Journal of Biomechanics, 45 (16), 2835-2840, 2012

 Namburete, A. I. L., Rana, M., Wakeling, J. M., “Computational methods for quantifying in vivo muscle fascicle curvature from ultrasound images”, Journal of Biomechanics, 44 (14), 2538-2543, 2011

 Wakeling, J. M., Jackman, M., Namburete, A. I. L., “The effect of external compression on the mechanics of muscle contraction”, Journal of Applied Biomechanics, 29 (3), 360-364, 2013

 Peer-Reviewed Conference Proceedings

 Namburete, A. I. L., Kemp, B., Papageorghiou, A. T., Noble, J. A., “Automated discovery of neurodevelopmental landmarks from 3D ultrasound images of the fetal brain”, Brain Informatics in Healthcare — Machine Learning Symposium, 30 August 2015, London, UK

 Huang, R., Namburete, A. I. L., Yaqub, M., Noble, J. A., “Automated mid-sagittal plane selection for corpus callosum visualization in 3D ultrasound images”, Proc. of Medical Image Analysis and Understanding (MIUA), 15-17 July 2014, Lincoln, UK

 Namburete, A. I. L., Yaqub, M., Kemp, B., Papageorghiou, A. T., Noble, J. A., “Predicting fetal neurodevelopmental maturation in ultrasound images”, Proc. of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), 14-18 September 2014, Boston, MA, USA

 Namburete, A. I. L., Stebbing, R. V., Noble, J. A., Diagnostic plane extraction from 3D parametric surface of the fetal cranium, Proc. of Medical Image Understating and Analysis (MIUA), 9-11 July 2014, London, UK, Podium Presentation

 Namburete, A. I. L., Stebbing, R. V., Noble, J. A., Cranial parametrization of the fetal head for 3D ultrasound image analysis, Proc. of the Medical Image Understating and Analysis (MIUA), July 2013, Birmingham, UK

 Namburete, A. I. L., Noble, J. A., Fetal cranial segmentation in 2D ultrasound images using shape properties of pixel clusters, Proc. of the International Society of Biomedical Imaging (ISBI), 7-11 April 2013, San Francisco, CA, USA, pp. 720-723

 Namburete, A. I. L., Rahmatullah, B., Noble, J. A., Nakagami-based choroid plexus detection in fetal ultrasound images using AdaBoost, Proc. of the Medical Image Understanding and Analysis (MIUA), July 2012, Swansea, Wales

 Chapman, G. H., Leung, J., Thomas, R., Namburete, A., Koren, Z., Koren, I., Projecting the area of in-field pixel defects based on pixel size, sensor

area, and ISO, Proceedings of SPIE 8298, January 2012, Burlingame, California, USA

 Chapman, G. H., Leung, Namburete, A. I. L., Predicting pixel defect rates based on image sensor parameters, IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, October 2011, Vancouver, Canada

 Invited Seminars

“A regression forest-based tool to predict gestational age from ultrasound images of the fetal brain”– Microsoft Research Cambridge, 15 June 2015