1 citations · 1 across the 4 of their papers we have counts for
5 papers
Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images
Pak-Hei Yeung, Moska Aliasi, Monique Haak +3
Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy f…
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…
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…
Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning
Pak-Hei Yeung, Ana I. L. Namburete, Weidi Xie
The objective of this work is to segment any arbitrary structures of interest (SOI) in 3D volumes by only annotating a single slice, (i.e. semi-automatic 3D segmentation). We show…