Vicente Grau Colomer
Director of the Centre for Doctoral Training in Healthcare Innovation
Professor of Engineering Science
Fellow of Mansfield College
Tel: 01865 610683
College: Mansfield College
Location: Oxford e-Research Centre, 7 Keble Road, Oxford OX1 3QG

Vicente Grau is an RCUK Academic Fellow at the Department of Engineering Science and the Oxford e-Research Centre, University of Oxford. He is associated with the Institute of Biomedical Engineering (IBME). He is also a Senior Research Fellow at Mansfield College.

He received his undergraduate degree in Electrical Engineering from the Universidad Politecnica de Valencia, Spain, in 1994, and his PhD in Medical Image Analysis in 2001. After spending two years as a postdoc at Brigham and Women’s Hospital, Harvard University and the ONH Biomechanics Lab at LSU Health Sciences Center in New Orleans, he joined the Medical Vision Laboratory in Oxford. He became an RCUK Academic Fellow in 2007.

Vicente’s research focuses on the development of biomedical image analysis algorithms, with an emphasis on the combination with computational models, and applications on cardiac and pulmonary medicine.

Pulmonary Imaging

Lung diseases are one of the main causes of mortality and disability worldwide. In recent years, hyperpolarized gas MRI has emerged as a promising new technology with the potential to improve diagnosis and potentially allow the development of new, better treatments. Before this promise is fulfilled, advances need to be made in both acquisition technology and image analysis. We are focusing in the image analysis side by using a combination of state-of-the-art image analysis methods and computational models, with the final aim of improving the understanding of image datasets and developing methods that link image values with the underlying lung function.

Cardiovasular imaging and modelling

Computational models are becoming a standard tool in many biomedical applications, and in particular in cardiovascular medicine. In the last years we have developed a pipeline to build computational models from high-resolution multimodal images. This includes the development of 3D histology dataset through registration with MRI scans, segmentation of relevant structures and mesh generation and the application of ionic models to investigate the relevance of small structures in electrical simulation results. Current research includes the extension of these methods to quantify intersubject variability, through the use of a standardized reference frame.

Biological image processing

Images are becoming ubiquitous in biological applications. Current image data volumes in biology labs no longer allow traditional visual analysis, and with the increasing use of high-throughput experiments there is a pressing need for robust, reusable biological image processing tools. Our current interests include the use of phase-based operation for curvilinear structure extraction in microscopy images.

A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents.
Chandrashekar A,  Handa A,  Lapolla P,  Shivakumar N,  Uberoi R,  Grau V,  Lee R,  et al. (2023)
Influence of myocardial infarction on QRS properties: a simulation study
Li L,  Camps J,  Wang Z,  Banerjee A,  Rodriguez B,  Grau V,  et al. (2023)
Deep learning segmentation of the right ventricle in cardiac MRI: the M&Ms challenge
Martin-Isla C,  Campello VM,  Izquierdo C,  Kushibar K,  Sendra-Balcells C,  Gkontra P,  Sojoudi A,  Fulton MJ,  Arega TW,  Punithakumar K,  Li L,  Sun X,  Al Khalil Y,  Liu D,  Jabbar S,  Queiros S,  Galati F,  Mazher M,  Gao Z,  Beetz M,  Tautz L,  Galazis C,  Varela M,  Hullebrand M,  Grau V,  et al. (2023)
Multi-modality cardiac image computing: a survey
Li L,  Ding W,  Huang L,  Zhuang X,  Grau V,  et al. (2023)
Understanding and improving risk assessment after myocardial infarction using automated left ventricular shape analysis
Corral Acero J,  Schuster A,  Zacur E,  Lange T,  Stiermaier T,  Backhaus SJ,  Thiele H,  Bueno Orovio A,  Lamata P,  Eitel I,  Grau Colomer V,  et al. (2022)
Predicting 3D cardiac deformations with point cloud autoencoders
Beetz M,  Ossenberg-Engels J,  Banerjee A,  Grau V,  et al. (2022)
Automated torso contour extraction from clinical cardiac MR slices for 3D torso reconstruction
Smith HJ,  Banerjee A,  Choudhury RP,  Grau V,  et al. (2022)