The Centre for Computational Medicine is a multidisciplinary, cross-faculty research centre that draws together research in physics, mathematics, computer science and medicine. It combines UCL’s formidable strengths in mathematical and computational modelling of disease to create a unique and world-leading centre. We support an interdisciplinary, dynamic, inclusive environment that enables disruptive and creative thinking.
Our work
We draw on many disciplines to enable our research, including physics, mathematics, biology, chemistry, computer science, engineering and statistics.ÌýMany of our projects lie at the intersection of biomedical imaging, mathematical modelling and machine learning.
Biomedical Imaging
We use biomedical imaging to guide computational analysis, alongside developing novel, cutting-edge imaging techniques with a focus on translation and providing patient benefit. Ongoing project areas include:
Magnetic Resonance Imaging (MRI) of Cancer
We are developingÌýquantitative MRI techniques to characterise the tumour microenvironment and predict drug delivery and treatment response. This includes techniques such as diffusion MRI modelling of tumour microstructure to non-invasively measure cell size and arterial spin labelling to measure vascular perfusion. ()
Imaging Transplanted Organs as Model Systems
We are passionate about understanding, validating and translating new imaging approaches, and are developing methods for trialling new technologies in transplanted human organs that are maintained by mechanical perfusion. This provides a novel platform to investigate real tumours in situ and how they interact with imaging biophysics (collaboration with Royal Free Hospital and WEISS).
Optical Imaging
We develop new optical imaging solutions such as high-resolution episcopic microscopy (HREM) and light-sheet microscopy. This allows us to create large-scale digital tissue models (‘digital twins’) that can be used for mathematical modelling and for better understanding biomedical imaging techniques.
HiP-CT
Mapping human organs at multiple scales using synchrotron radiation. ()
Mathematical Modelling
We use mathematical modelling to explore and quantify the relationship between function and form.
Blood Flow and Solid Mechanics in Tumours
REANIMATE is our framework for combining three-dimensional microscopy of tumours and in vivo imaging, to model blood flow, and interstitial fluid dynamics to better understand and optimise drug delivery to tumours. The interaction between fluid mechanics and solid mechanics is an active area of investigation, particularly in relation to metastatic growth in the spine (collaboration with Leeds University). ()
Blood Vessel Network Simulation
We have developed several platforms for modelling normal blood vessel networks, using biophysical principles such as Murray’s Law, alongside models of aberrant growth such as angiogenesis in tumours.
OncoEng
An EPSRC-funded project in collaboration with theÌýUniversity of Leeds, aiming to predict fracture risk in spinal metastases. ().
Retinal modelling
We are creating detailed, large-scale models of the human retina, using ophthalmology image data, to better understand the link between vascular delivery and metabolic demand (collaboration with Moorfields Eye Hospital and Institute of Ophthalmology). Understanding the cause of loss of vascular perfusion in diabetic retinopathy is a specific motivation and its impact on vision.Ìý
Liver Perfusion
Using a range of MRI approaches, we are developing large-scale models of liver vascular perfusion, particularly during the growth of tumours, and investigating the delivery of therapeutic nanoparticles (collaboration with Royal Free Hospital and UCL Chemistry).
Machine learning / Artificial Intelligence
Physics informed deep learning
Our imaging and modelling work combinesÌýphysics-informed deep learning, in which mathematical models are used to train generative learning algorithms. For example, working with colleagues at Moorfields Eye Hospital, we are developing deep generative modelling approaches to segment and classify images from the retina.
Supervised learning
We have developed a large imaging database of vascular imaging data with manual labels, which forms the basis of our tUbe-Net framework for segmenting blood vessel networks with minimal manual labelling ().
Gonçalves MR, Johnson SP, Walker-Samuel S. Wellcome Open Res 2017, 2:38
Gonçalves MR, Peter Johnson S,ÌýWalker-Samuel S, et al. Br J Cancer. 2016 Jun 14;114(12):e13
Funding and Partnerships
Interested in joining us?
Researchers: We regularly have funding for postdoctoral positions and welcome prospective applications from talented postdoctoral candidates seeking to obtain their own funding. To discuss potential opportunities, please contact Prof. Simon Walker-Samuel (simon.walkersamuel@ucl.ac.uk) or Prof. Rebecca Shipley (rebecca.shipley@ucl.ac.uk). Please include a CV with your email.
Students: If you have a good academic record and would like to discuss opportunities in our Centre, please contact Prof. Simon Walker-Samuel (simon.walkersamuel@ucl.ac.uk) or Prof Rebecca Shipley (rebecca.shipley@ucl.ac.uk), including a CV with your email.