Madeleine K Wyburd

I am a Danish Data Science Academy (DDSA) postdoctoral fellow, working in the recently established LNN@UCPH at the University of Copenhagen. I am an associate member of Oxford Machine Learning NeuroImaging Lab (OMNI) at the University of Oxford, with Prof Ana Namburete.

Email  /  Google Scholar  /  Github

In my current role, I am developing deep learning methods to monitor fetal and infant brain development. I am particularly interested in the early detection of cerebral palsy from neuroimaging. 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.

Normative growth trajectories of 28 fetal brain phenotypes derived from 4205 3D ultrasound scans collected across 7 international sites. The low variance between sites reinforces the principle that the brain develops similarly when environmental constraints are minimal.

Comparing ultrasound and MRI fetal brain features from same day scanning.

Extension from the 2021 MICCAI paper.

Novel segmentation method guaranteeing accurate topology.

More Information Coming Soon

More Information Coming Soon

Initial cortical plate segmentation methods