Building analysis into the image processing pipeline through machine learning to detect healthy anatomy and anomalies, monitoring disease progression and improving navigation
What does this
Common operations applied to medical images involve various forms of image registration and segmentation. Often, these are considered as ad hoc steps within some form of processing pipeline, with little or no attempt to consolidate or generalise the principles. A more rigorous approach involves formulating all these operations within a unifying probabilistic generative model of the data. Of particular importance to medical imaging is the incorporation of spatial deformations within the model, along with methods for encoding inter-subject variability. There is still much work to be done in the area of biomedical image analysis, image datasets are large and models are complex, so numerous approximations are necessary – although a continuation of Moore’s Law would allow much more to be achieved. In general, those models that most accurately encode real biological phenomena (basic science applications) are likely to be the most useful for making predictions on which real-world medical decisions may be made (applied science). In addition to addressing the immediate short term needs of biologists and clinicians, imaging scientists should also be laying the groundwork for a future in which harnessing `big data’ in the NHS will revolutionise healthcare.
Partners and Facilities
This methodology connects with Clinical Themes across the CDT, with a focus on Neuroimaging and Cancer Imaging.
Studying Under this theme
Projects listed below are available for applicants to include in their 5 project choices. Successful candidates will meet with both primary supervisors from their selection as part of the induction process before being assigned to an allocated research project. To view all available project videos please visit our YouTube channel.
I am one of the developers of the SPM software, which is used internationally by several thousand brain imaging researchers. Contributions to SPM relate to generative models for brain image registration and tissue segmentation (as well as informatics issues such as DICOM and other image file formats, ontology descriptions of processing procedures, user interfaces etc).
My current interests involve applying the computational anatomy framework of Grenander and others, in conjunction with pattern recognition approaches, to obtain accurate characterisations of anatomical differences among populations of subjects. This work has potential for translation into clinical practice, and relies on the establishment of large databases of scans. The emphasis is on models with predictive accuracy, rather than simple idealisations.
More about our Methodological Portfolio
- Industry Seminar - "icoMetrix: Bringing MRI biomarkers to the MS patient"
- From Concept to Reality (PhD to CEO), the Story of a Medical Imaging Company
- Dr Jonathan Rohrer at the British Science Festival
- Recruitment time for the EPSRC CDT in Medical Imaging
- Industry Seminar: The Deep Learning Phenomenon
- Medical Image Computing Summer School launches July 2016
- The Future of Musculoskeletal Imaging
- CDT Student Guotai Wang wins the Departmental PhD Prize
- Molecular Imaging in the Clinic - Status, Opportunities & Issues
- CDT Students complete the Royal Parks Half Marathon to fundraise for Bliss
- Call for Applications - 2016 Hamlyn Winter School on Surgical Imaging and Vision
- Disruptive innovation in paediatric cardiovascular magnetic resonance – the importance of physics and computer science
- Visit us at the UCL Graduate Open Day
- Merit Abstract Award for student paper on multimodal imaging for Alzheimer's
- Student research accepted for presentation at IPMI 2017
- Image-guided treatment of pancreaticobiliary tumours
- Seeing the brain: How neuroimaging transforms the diagnosis and treatment of patients with brain disorders
- Developing novel biomarkers in the GENetic FTD Initiative (GENFI) study
- Image Analysis for Studying Radiotherapy Induced Lung Damage