Madeleine K Wyburd

I am a postdoctoral researcher in the recently established Oxford Machine Learning NeuroImaging Lab (OMNI) at the University of Oxford, with Prof Ana Namburete.

I recently finished my DPhil, which focused on analysing medical images using deep learning, resulting in my thesis titled: Preserving Known Anatomical Topology in Medical Image Segmentation Using Deep Learning

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Research

In my current role, I am developing deep-learning methods to monitor fetal brain development from ultrasound volumes. During my DPhil, I developed a fully automated pipeline to characterise the healthy cortical development. I am now looking at at-risk pregnancies to see how their development varies.







TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
Madeleine K Wyburd , Nicola Dinsdale, Ana IL Namburete, Mark Jenkinson
MICCAI, 2021 [Early Acceptance]- Conference Proceedings + Presentation

Project Page / Paper / Code

Novel segmentation method guaranteeing accurate topology.

Automated grading of fetal cortical development using deep-learning algorithms: A preliminary 3D ultrasound study in healthy fetuses
Madeleine K Wyburd , Linde Hesse,Moska Aliasi, Felipe Moser, Monique C. Haak, Aris Papageorghiou, the INTEGROWTH-21st Consortium, Ana IL Namburete
ISUOG, 2021 - Oral Presentation

More Information Coming Soon

Deep learning-based assessment of second trimester cortical plate development in 3D ultrasound
Madeleine K Wyburd , Aris Papageorghiou,Mark Jenkinson Ana IL Namburete
ISUOG, 2021 - Oral Presentation

More Information Coming Soon

Using Deep Learning to Segment the Developing Cortical Plate from 3D Fetal Ultrasound.
Madeleine K Wyburd , Mark Jenkinson Ana IL Namburete
RSNA, 2020 - Poster Presentation
Cortical Plate Segmentation using CNNs in 3D Fetal Ultrasound
Madeleine K Wyburd, Ana IL Namburete, Mark Jenkinson
MIUA, 2020 - Conference Proceedings + Presentation

Paper / Presentation

Initial cortical plate segmentation methods


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