Scholar
Nicola K Dinsdale
Google Scholar ID: SsccznQAAAAJ
University of Oxford
Citations & Impact
Citations
699
H-index
14
i10-index
16
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Paper: Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound, ISBI 2025
- Paper: Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images, Winner of LISA Challenge 2024 @ MICCAI 2024
- Paper: UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data, bioRxiv, 2024
- Paper: Anatomically plausible segmentations: Explicitly preserving topology through prior deformations, Medical Image Analysis 2024
- Paper: QAERTS: Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos, MICCAI 2024 [Early Acceptance - top 11%]
Research Experience
- Working in the OMNI lab, focusing on deep learning in medical imaging, robust segmentation, and domain adaptation.
Education
- Studied for my DPhil (PhD) in the Analysis Group at the Wellcome Centre for Integrative Neuroimaging at the University of Oxford, where I researched deep learning based approaches for neuroimaging analysis, supervised by Prof. Mark Jenkinson and Dr. Ana Namburete, funded by the UKRI EPRSC/MRC as part of the ONBI DTC.
Background
- Currently working as a post-doctoral research associate in the Oxford Machine Learning in NeuroImaging Lab (OMNI), working with Dr. Ana Namburete, in the Department of Computer Science. My research uses computer vision and deep learning to solve medical imaging problems. I am especially interested in exploring methods to overcome the barriers to clinical translatability of deep learning methods and robust deep learning, and I am open to collaboration opportunities.
Miscellany
- Email / Google Scholar / Github
