Healthcare technologies

Multi-scale, multi-parametric data acquisition and analysis for healthcare.

Advanced sensor technology collects vast amounts of data about a range of biological processes, relevant specifically to medicine and healthcare. We are developing algorithms to acquire and analyse such data to improve our understanding of such processes and enable medical diagnosis and treatment of diseases.

On a molecular scale, information about biochemical mechanisms helps us to understand normal and abnormal processes in the human body. Knowledge about such processes improves our understanding of how parts of the human body works, such as the brain. It also enables to model the mechanisms of diseases, to develop diagnosis and treatment techniques.

Similarly, on larger scales, involving a whole human or even groups of humans, we are able to monitor and characterise human behaviour. For example, we may monitor human activity with wearable devices to detect emergencies or slowly developing conditions. We may also collect data about human task performance, for example to model and predict the efficiency of medical diagnosis and improve diagnosis procedures.

Our group has expertise in control, modelling, simulation, machine learning, geometry, video and image processing and has a range of active projects across the scales, data sources and algorithms. Ultimately we envisage linking the processes across scales and different data sources to enable a more complete understanding of biological processes in healthcare.

Aims

Devise quantum control techniques to compute pulse sequences for magnetic resonance spectroscopy and imaging based on user-defined targets, such as identifying and quantifying specific metabolites.
Develop methods to characterise bio-chemical mechanisms and learn functional models from empirical data.
Early stage cancer and dementia diagnosis from multi-parametric data.

Our research can be described under the following headings:

Magnetic resonance imaging and spectroscopy (MRI/S)

Magnetic resonance spectroscopy and imaging holds the promise to identify specific metabolites using techniques from quantum control which are commonly used in Nuclear Magnetic Resonance (to identify chemical composition, molecule structures, etc.)

However, practical considerations of imaging and spectroscopy in patients (complex biological environments at room temperature compared to the highly controlled environments of Nuclear Magnetic Resonance) cause many uncertainties which makes it hard to obtain reliable data or any usable data at all.

Recent approaches from robust quantum control deep learning and advances in sensing technology help to improve these techniques to obtain more reliable information about specific metabolites. This in turn can help to identify biomarkers for early diagnosis of diseases such as cancer and dementia. It also builds the basis to obtain quantified data to build functional models of biochemical processes.

We are developing control techniques to compute custom magnetic resonance imaging and spectroscopy pulse sequences, based on user-defined targets, such as quantifying specific metabolites. Quantification of the data acquired with such techniques is done using traditional spectral analysis techniques as well as deep learning.

Medical image segmentation

We work on medical image segmentation using deep learning. We work on techniques for brain tumour and stroke lesion segmentation and develop techniques to detect early signs of Alzheimer's disease based on MRI, CT and PET images. We further devise methods for computer-aided diagnosis of early-stage prostate cancer and lung cancer diagnosis and treatment planning based on CT.

Human task performance

We aim to develop computational models that can automatically and reliably predict the task performance of the radiologist in the interpretation (such as lesion detection) of medical images. These models will be used either to support the human to augment diagnostic efficiency, or to train the human towards improved diagnostic accuracy.

Selected publications

Yang, J. et al., 2020. Self-paced balance learning for clinical skin disease recognition. IEEE Transactions on Neural Networks and Learning Systems 31 (8), pp.2832-2846. (10.1109/TNNLS.2019.2917524)
Lévêque, L. , Young, P. and Liu, H. 2020. Studying the gaze patterns of expert radiologists in screening mammography: a case study with Breast Test Wales. Presented at: 28th European Signal Processing Conference (EUSIPCO 2020) Amsterdam, Netherlands 18-22 Janurary 2021.
Jenkins, C. et al. 2019. Quantification of edited magnetic resonance spectroscopy: a comparative phantom based study of analysis methods. Presented at: ISMRM 27th Annual Meeting & Exhibition Montréal, QC, Canada 11-16 May 2019. , pp.-.
Yu, Y. et al., 2018. Perceptual quality and visual experience analysis for polygon mesh on different display devices. IEEE Access 6 , pp.42941-42949. (10.1109/ACCESS.2018.2859254)
Leveque, L. et al. 2018. State of the art: Eye-tracking studies in medical imaging. IEEE Access 6 , pp.37023-37034. (10.1109/ACCESS.2018.2851451)
Al Ali, A. et al., 2015. The influence of snoring, mouth breathing and apnoea on facial morphology in late childhood: a three-dimensional study. BMJ Open 5 (9) e009027. (10.1136/bmjopen-2015-009027)
Kajić, V. et al., 2010. Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Optics Express 18 (14), pp.14730-14744. (10.1364/OE.18.014730)
Legg, P. A. 2010. Multimodal retinal imaging: Improving accuracy and efficiency of image registration using Mutual Information. PhD Thesis , Cardiff University.

Funding

EPSRC

Project name: A BioEngineering approach for the SAFE design and fitting of Respiratory Protective Equipment (BE-SAFE RPE), 2020–2022, Paul Rosin and Dave Marshall (School of Computer Science and Informatics)
Principal investigator: Peter Worsley, University of Southampton

Project name: Computational modelling and prediction of brain shift to improve surgical navigation

EPSRC Industrial Case PhD Studentship, Prof Sam Evans (ENGIN, co-PI) Prof D. Marshall (co-PI) , £120K. Start Date Oct 2015, duration 3.5 Years.
PSE college COMSC funded PhD studentship (3 Years)
In collaboration with Renishaw PLC. Cardiff School of Engineering, Cardiff University Brain Imaging Centre (CUBRIC) and Cardiff School of Computer Science and Informatics: each school also supplying a PhD studentship. Setting up a new interdisciplinary research group in Neurosurgical

KTP

Project name: Integration of video realistic avatar capability into an artificial intelligence driven healthcare information platform, 2019–2020, Paul Rosin and Dave Marshall