25 citations · 54 across the 10 of their papers we have counts for
10 papers
Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images
Michelle A Bartolo, Alyssa M Taylor-LaPole, Darsh Gandhi +9
Patient-specific computational modeling is a popular, non-invasive method to answer medical questions. Medical images are used to extract geometric domains necessary to create thes…
Temporal extrapolation of heart wall segmentation in cardiac magnetic resonance images via pixel tracking
Arash Rabbani, Hao Gao, Dirk Husmeier
In this study, we have tailored a pixel tracking method for temporal extrapolation of the ventricular segmentation masks in cardiac magnetic resonance images. The pixel tracking pr…
The Fully Convolutional Transformer for Medical Image Segmentation
Athanasios Tragakis, Chaitanya Kaul, Roderick Murray-Smith +1
We propose a novel transformer model, capable of segmenting medical images of varying modalities. Challenges posed by the fine grained nature of medical image analysis mean that th…
Fast Parameter Inference in a Biomechanical Model of the Left Ventricle using Statistical Emulation
Vinny Davies, Umberto Noè, Alan Lazarus +5
A central problem in biomechanical studies of personalised human left ventricular (LV) modelling is estimating the material properties and biophysical parameters from in-vivo clini…
Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries
Mitchel J. Colebank, L. Mihaela Paun, M. Umar Qureshi +4
Computational fluid dynamics (CFD) models are emerging as tools for assisting in diagnostic assessment of cardiovascular disease. Recent advances in image segmentation has made sub…
On a New Improvement-Based Acquisition Function for Bayesian Optimization
Umberto Noè, Dirk Husmeier
Bayesian optimization (BO) is a popular algorithm for solving challenging optimization tasks. It is designed for problems where the objective function is expensive to evaluate, per…