21 citations · 36 across the 9 of their papers we have counts for
9 papers
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners pr…
Segmentation method for cerebral blood vessels from MRA using hysteresis
Georgia Kenyon, Stephan Lau, Michael A. Chappell +1
Segmentation of cerebral blood vessels from Magnetic Resonance Imaging (MRI) is an open problem that could be solved with deep learning (DL). However, annotated data for training i…
FedHarmony: Unlearning Scanner Bias with Distributed Data
Nicola K Dinsdale, Mark Jenkinson, Ana IL Namburete
The ability to combine data across scanners and studies is vital for neuroimaging, to increase both statistical power and the representation of biological variability. However, com…
Uncertainty categories in medical image segmentation: a study of source-related diversity
Luke Whitbread, Mark Jenkinson
Measuring uncertainties in the output of a deep learning method is useful in several ways, such as in assisting with interpretation of the outputs, helping build confidence with en…
Mutual information neural estimation for unsupervised multi-modal registration of brain images
Gerard Snaauw, Michele Sasdelli, Gabriel Maicas +4
Many applications in image-guided surgery and therapy require fast and reliable non-linear, multi-modal image registration. Recently proposed unsupervised deep learning-based regis…
TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee Topology Preservation in Segmentations
Madeleine K. Wyburd, Nicola K. Dinsdale, Ana I. L. Namburete +1
Accurate topology is key when performing meaningful anatomical segmentations, however, it is often overlooked in traditional deep learning methods. In this work we propose TEDS-Net…
Challenges for machine learning in clinical translation of big data imaging studies
Nicola K Dinsdale, Emma Bluemke, Vaanathi Sundaresan +3
The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology. However, despite the su…
Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging
Minh-Son To, Ian G Sarno, Chee Chong +2
Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant…
Brain tumour segmentation using a triplanar ensemble of U-Nets
Vaanathi Sundaresan, Ludovica Griffanti, Mark Jenkinson
Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challen…