1 citations · 3 across the 11 of their papers we have counts for
12 papers
Shadow Reduction in Ultrasound Imaging Using Differentiable Simulation and Radiance Field Decomposition
Valentin Bacher, Pak Hei Yeung, Bernhard Kainz +4
Acoustic shadows from bone and other highly attenuating tissues obscure clinically important structures in ultrasound. In fetal brain imaging, skull-induced artefacts disproportion…
Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos
Jieyun Bai, Zihao Zhou, Yitong Tang +60
A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income c…
Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation
Hoda Kalabizadeh, Ludovica Griffanti, Pak-Hei Yeung +3
Deep learning models for medical image segmentation often struggle when deployed across different datasets due to domain shifts - variations in both image appearance, known as styl…
Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images
Jayroop Ramesh, Valentin Bacher, Mark C. Eid +6
The International Society of Ultrasound advocates Intrapartum Ultrasound (US) Imaging in Obstetrics and Gynecology (ISUOG) to monitor labour progression through changes in fetal he…
Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound
Joshua Omolegan, Pak Hei Yeung, Madeleine K. Wyburd +5
Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions…
Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain MR images
Vaanathi Sundaresan, Nicola K Dinsdale
Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many fac…
Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos
Jayroop Ramesh, Nicola K Dinsdale, the INTERGROWTH-21st Consortium +2
Accurately localizing two-dimensional (2D) ultrasound (US) fetal brain images in the 3D brain, using minimal computational resources, is an important task for automated US analysis…
Prototype Learning for Explainable Brain Age Prediction
Linde S. Hesse, Nicola K. Dinsdale, Ana I. L. Namburete
The lack of explainability of deep learning models limits the adoption of such models in clinical practice. Prototype-based models can provide inherent explainable predictions, but…
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…
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…