Oxford Machine Learning in NeuroImaging Lab

Oxford Machine Learning in NeuroImaging Lab

Department of Computer Science, University of Oxford

Our interests are in designing and implementing data analytics and artificial intelligence (AI) to handle large medical datasets, with a core interest in characterising spatial and temporal structural changes in the human brain during the perinatal period and in late adulthood.

Our research encompasses studying human physiology and software development, with an emphasis on creating scalable, practical, and theoretically sound tools for mining diagnostic information from clinical datasets. One of our key research themes seeks to enhance the use of challenging, but information-rich, ultrasound for quantification of structural brain development in early life.

We also develop machine learning-based algorithms that address the key challenges posed by increasingly large and diverse medical datasets, including label scarcity, model harmonisation, and model explainability.

More information about ongoing projects may be found on this website.

Group Members

Ana Namburete

Group Leader

Ana Namburete

Group Leader

Lorenzo Venturini

DPhil Student

Lorenzo Venturini

DPhil Student

Nicola Dinsdale

DPhil Student

Nicola Dinsdale

DPhil Student

Hugo Yeung

DPhil Student

Hugo Yeung

DPhil Student

Felipe Moser

DPhil Student

Felipe Moser

DPhil Student

Nora Vogt

DPhil Student

Nora Vogt

DPhil Student

Madeleine Wyburd

DPhil Student

Madeleine Wyburd

DPhil Student

Linde Hesse

DPhil Student

Linde Hesse

DPhil Student

Tom Waddell

DPhil Student

Tom Waddell

DPhil Student

Andrei Claudiu-Roibu

DPhil Student

Andrei Claudiu-Roibu

DPhil Student

Marianne van der Vaart

DPhil Student

Marianne van der Vaart

DPhil Student

Selected Publications

2021

TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
Wyburd, M. K., Dinsdale, N. K., Namburete, A. I. L., Jenkinson, M.
In: Proc. of Medical Image Computing and Computer-Assisted Interventions (MICCAI) (to appear) [Early Acceptance]
Project page | Code

Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning
Yeung, P. H., Namburete, A. I. L., Xie, W.
In: Proc. of Medical Image Computing and Computer-Assisted Interventions (MICCAI) (to appear)
Project page | Code

Learning to Map 2D Ultrasound Images into 3D Space with Minimal Human Annotation
Yeung, P. H., Aliasi, M., Papageorghiou, A. T., Haak, M., Xie, W., Namburete, A. I. L.
In: Medical Image Analysis
Project page | Code | PDF

Deep learning-based unlearning of dataset bias for MRI harmonisation and confound removal
Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L.
In: NeuroImage
Project page | Code | PDF

Learning patterns of the ageing brain in MRI using deep convolutional networks
Dinsdale, N., Bluemke, E., Smith, S. M., Arya, Z., Vidaurre, D., Jenkinson, M., Namburete, A. I. L.
In: NeuroImage
Project page | Code | PDF

2020

Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
Jiao, J., Namburete, A. I. L., Papageorghiou, A. T., Noble, J. A.
In: IEEE Transactions of Medical Imaging
arXiv

The association between flow and oxygenation and cortical development in fetuses with congenital heart defects using a brain-age prediction algorithm
Everwijn, S. M. P., Namburete, A. I. L., van Geloven, N., Jansen, F. A. R., Papageorghiou, A. T., Teunissen, A. K. K., Rozendaal, L., Blom, N. A., van Lith, J. M. M., Haak, M. C.
In: Prenatal Diagnosis
PDF

Low-Memory CNNs Enabling Real-Time Ultrasound Segmentation Towards Mobile Deployment
Vaze, S., Xie, W., Namburete, A. I. L.
In: IEEE Journal of Biomedical Health Informatics
Project page | Code | PDF

Unlearning scanner bias for MRI harmonisation
Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L.
In: Proc. of Medical Image Computing and Computer-Assisted Interventions (MICCAI) [Early Acceptance]
Code | PDF

Uncertainty estimates a data selection criteria to boost omni-supervised learning
Venturini, L., Noble, J. A., Papageorghiou, A. T., Namburete, A. I. L.
In: Proc. of Medical Image Computing and Computer-Assisted Interventions (MICCAI) [Early Acceptance]
PDF

Improving U-Net Segmentation with Active Contour Based Label Correction
Hesse, L. S., Namburete, A. I. L.
In: Proc. of Medical Image Understanding and Analysis (MIUA)
Video | PDF

Cortical Plate Segmentation using CNNs in 3D Fetal Ultrasound
Wyburd, M. K., Jenkinson, M., Namburete, A. I. L.
In: Proc. of Medical Image Understanding and Analysis (MIUA)
Video | PDF

Segmenting Hepatocellular Carcinoma in Multi-Phase CT
Vogt, N., Ridgeway, G., Brady, M., Namburete, A. I. L.
In: Proc. of Medical Image Understanding and Analysis (MIUA)
Video | PDF

Unlearning Scanner Bias for MRI Harmonisation in Medical Image Segmentation
Dinsdale, N. K., Jenkinson, M., Namburete, A. I. L.
In: Proc. of Medical Image Understanding and Analysis (MIUA)
Code | Video |PDF

2019

Cortical development in fetuses with congenital heart defects using an automated brain-age prediction algorithm
Everwijn, S. M. P., Namburete, A. I. L., van Geloven, N., Jansen, F. A. R., Papageorghiou, A. T., Noble, J. A., Teunissen, A. K. K., Rozendaal, L., Blom, N. A., van Lith, J. M. M., Haak, M. C.
In: Acta Obstetrica et Gynecologica Scandanavica
PDF

Anatomy-aware self-supervised fetal MRI synthesis from unpaired ultrasound images
Jiao, J., Namburete, A. I. L., Papageorghiou, A. T., Noble, J. A.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI) -- Workshop on Machine Learning in Medical Imaging (MLMI)
PDF

SWANS: Spatial Warping Network for Hippocampus Segmentation
Dinsdale, N., Jenkinson, M., Namburete, A. I. L.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI) [Early Acceptance]
PDF

Automated Fetal Brain Extraction from Clinical Ultrasound Volumes using 3D Convolutional Neural Networks
Moser, F., Huang, R., Papiez, B. W., Papageorghiou, A. T., Namburete, A. I. L.
In: Proc. of Medical Image Analysis and Understanding (MIUA)
PDF

Multi-task CNN for Structural Semantic Segmentation in 3D Fetal Brain Ultrasound
Venturini, L., Noble, J. A., Namburete, A. I. L.
In: Proc. of Medical Image Analysis and Understanding (MIUA)
PDF

2018

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning
Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., Noble, J. A.
In: Medical Image Analysis
PDF

Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region- based descriptor
Huang, R., Namburete, A. I. L., Noble, J. A.
In: Journal of Medical Imaging
PDF

Multi-channel Groupwise Registration to Construct an Ultrasound-Specific Fetal Brain Atlas
Namburete, A. I. L., van Kampen, R., Papageorghiou, A. T., Papiez, B. W.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI)-- Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI) [Best Paper Award]
PDF

Segmentation of Fetal Adipose Tissue using Efficient CNNs for Portable Ultrasound
Vaze, S., Namburete, A. I. L.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI)-- Workshop on Perinatal, Preterm, and Paediatric Image Analysis (PIPPI)
PDF

Omni-Supervised Learning: Scaling Up to Large Unlabelled Medical Datasets
Huang, R., Noble, J. A., Namburete, A. I. L.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI)
PDF

2017

Robust regression of brain maturation from 3D fetal neurosonography using CRNs
Namburete, A. I. L., Xie, W., Noble, J. A.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI)-- Workshop on Fetal and Infant Imaging (FIFI) [Best Paper Award]
PDF

2015

Learning-based prediction of gestational age from ultrasound images of the fetal brain
Namburete, A. I. L., Stebbing, R. V., Yaqub, M., Kemp, B., Papageorghiou, A. T., Noble, J. A.
In: Medical Image Analysis
PDF

Data-driven shape parameterization for segmentation of the right ventricle from 3D+t echocardiography
Huang, R., Namburete, A. I. L., Noble, J. A.
In: Medical Image Analysis
PDF

2014

Automated mid-sagittal plane selection for corpus callosum visualization in 3D ultrasound images
Huang, R., Namburete, A. I. L., Yaqub, M., Noble, J. A.
In: Proc. of Medical Image Analysis and Understanding (MIUA)
PDF

Predicting fetal neurodevelopmental maturation in ultrasound images
Namburete, A. I. L., Yaqub, M., Kemp, B., Papageorghiou, A. T., Noble, J. A.
In: Proc. of Medical Image Computing and Computer-Assisted Intervention (MICCAI)
PDF

Diagnostic plane extraction from 3D parametric surface of the fetal cranium
Namburete, A. I. L., Stebbing, R. V., Noble, J. A.
In: Proc. of Medical Image Understating and Analysis (MIUA)
PDF

2013

Nakagami-based AdaBoost learning framework for detection of anatomical landmarks in 2D fetal neurosonograms
Namburete, A. I. L., Noble, J. A.
In: Special Issue of the British Machine Vision Association (BMVA) [Invited contribution]
PDF

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

Fetal cranial segmentation in 2D ultrasound images using shape properties of pixel clusters
Namburete, A. I. L., Noble, J. A.
In: Proc. of International Society of Biomedical Imaging (ISBI)
PDF

The effect of external compression on the mechanics of muscle contraction
Wakeling, J. M., Jackman, M., Namburete, A. I. L.
In: Journal of Applied Biomechanics
PDF

2012

Nakagami-based choroid plexus detection in fetal ultrasound images using AdaBoost
Namburete, A. I. L., Rahmatullah, B., Noble, J. A.
In: Proc. of Medical Image Understating and Analysis (MIUA)
PDF