Open-science in Infant MRI: available infant MRI datasets and analysis pipelines
Organisation of Human Brain Mapping (OHBM) · 2026
Madeleine K Wyburd
DDSA postdoctoral fellow at LNN@UCPH, University of Copenhagen, and proudly an associate member of the Oxford Machine Learning NeuroImaging Lab (OMNI) with Prof Ana Namburete.
Research. I develop deep learning methods to monitor fetal and infant brain development, with a focus on early detection of cerebral palsy from neuroimaging. During my DPhil I built a fully automated pipeline to characterise healthy cortical development; I now study at-risk pregnancies to see how their development diverges.
News
- 2026 · AugAccepted into The Lundbeck Foundation Investigator Network
- 2026 · AugUpdated website
- 2026 · Jul Attended OHBM in Bordeaux and FITNG in Panama.
Imaging the fetal brain with ultrasound: bridging research innovation and clinical practice
BMJ Research in Practice · 2026
Deep learning in fetal, infant, and toddler neuroimaging research
Developmental Cognitive Neuroscience · 2025
Anatomically Plausible Segmentations: Explicitly Preserving Topology Through Prior Deformations
Medical Image Analysis · 2024
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
MICCAI · 2021 · Early Acceptance, Oral
Automated grading of fetal cortical development using deep-learning algorithms: A preliminary 3D ultrasound study in healthy fetuses
ISUOG · 2021 · Oral
Deep learning-based assessment of second trimester cortical plate development in 3D ultrasound
ISUOG · 2021 · Oral
Using Deep Learning to Segment the Developing Cortical Plate from 3D Fetal Ultrasound
RSNA · 2020 · Poster
