Medical image computing for next-generation healthcare technology
Lead Research Organisation:
University College London
Department Name: Computer Science
Abstract
At the heart of this platform is a vision of the future of medical technology enabled by the increasing availability of rich and diverse data from large-scale data collection initiatives. The vision is to exploit this data mass to enrich sparse data acquired at point of care. For example, a single clinical MRI or ultrasound scan, together with subject-specific clinical data (age, sex, symptoms, genetics) can index a centralised data mass to infer likely diagnosis, prognosis, and treatment outcomes by matching to similar individuals about whom much more is known. The same paradigm further enables cheap and/or small and/or low-power devices able to acquire data in non-specialist locations, such as portable or hand-held scanners, or tiny imaging devices in surgical instruments.
Platform funding will maintain our world-leading activities in a range of medical image computing topics, support alignment of currently distinct strands of work for mutual short-term benefit, and develop key enabling technology and demonstrators of our long-term vision. Our current work develops state of the art imaging technology, image analysis techniques, and mathematical and computational models that maximise the information contained in and derived from large data sets. We also develop a range of automated diagnostic systems and surgical support systems that can demonstrate the benefits of the data-driven paradigm.
Platform funding supports the career development of the applicants, a pool of associated academic staff, talented post-doctoral researchers, and students coming through an associated EPSRC Centre for Doctoral Training in Medical Imaging. The platform provides opportunities for all to develop new ideas crossing boundaries between different topics, while contributing to a central long-term vision that supports a variety of future research careers. Competitive allocation of resource under close mentorship of senior colleagues instils early-career researchers with essential academic skills required for successful future careers. It further enhances the career progression of our senior staff by supporting opportunities to acquire new skills, establish new collaborations, explore commercialisation, and recruit the best staff from around the world when opportunities arise.
Platform funding will maintain our world-leading activities in a range of medical image computing topics, support alignment of currently distinct strands of work for mutual short-term benefit, and develop key enabling technology and demonstrators of our long-term vision. Our current work develops state of the art imaging technology, image analysis techniques, and mathematical and computational models that maximise the information contained in and derived from large data sets. We also develop a range of automated diagnostic systems and surgical support systems that can demonstrate the benefits of the data-driven paradigm.
Platform funding supports the career development of the applicants, a pool of associated academic staff, talented post-doctoral researchers, and students coming through an associated EPSRC Centre for Doctoral Training in Medical Imaging. The platform provides opportunities for all to develop new ideas crossing boundaries between different topics, while contributing to a central long-term vision that supports a variety of future research careers. Competitive allocation of resource under close mentorship of senior colleagues instils early-career researchers with essential academic skills required for successful future careers. It further enhances the career progression of our senior staff by supporting opportunities to acquire new skills, establish new collaborations, explore commercialisation, and recruit the best staff from around the world when opportunities arise.
Planned Impact
Medical image computing is a linchpin of modern medicine and biomedical research. It provides the mathematical and computational models and algorithms that link biomedical image data to an understanding of tissue-and-body structure and function in health and disease. This is the foundation for current and future drug/treatment discovery, development, and deployment.
Our work impacts most directly on neurological diseases and cancer. The UK dementia platform estimates annual socioeconomic costs of dementia in Britain at around £17B. A treatment prolonging independent life of dementia patients by just one year would save around £1B per year in care costs as well as boosting the UK economy through revenue from the treatment if realised through its pharmaceutical industry and thriving community of related SMEs. Annual costs of cancer have similar scale; our work in early diagnostics and treatment delivery offers similar socio-economic impact.
A key impact of our long-term vision of medical technology is to make accurate diagnostic and prognostic testing widely accessible in non-specialist scenarios. For example, measurements from a handheld scanner at a local doctor's surgery, pharmacy, or field hospital, coupled with clinical data link to a central information resource to obtain suggestions of diagnosis, prognosis, treatment assignment and/or further testing strategy. This reduces the cost of patient examinations, reduces the need for invasive tests, and decreases the delay in moving from assessment to treatment. Ultimately for the patient, this leads to cheaper, faster and more accurate diagnosis, more reliable prognosis, reduction of side effects from diagnostic tests, and better-targeted treatment and care. Economically, it stimulates industry to develop and market new drugs and devices for assessment and treatment. This in turn reduces the burden on the healthcare system by prolonging independent good-quality life.
Our work impacts most directly on neurological diseases and cancer. The UK dementia platform estimates annual socioeconomic costs of dementia in Britain at around £17B. A treatment prolonging independent life of dementia patients by just one year would save around £1B per year in care costs as well as boosting the UK economy through revenue from the treatment if realised through its pharmaceutical industry and thriving community of related SMEs. Annual costs of cancer have similar scale; our work in early diagnostics and treatment delivery offers similar socio-economic impact.
A key impact of our long-term vision of medical technology is to make accurate diagnostic and prognostic testing widely accessible in non-specialist scenarios. For example, measurements from a handheld scanner at a local doctor's surgery, pharmacy, or field hospital, coupled with clinical data link to a central information resource to obtain suggestions of diagnosis, prognosis, treatment assignment and/or further testing strategy. This reduces the cost of patient examinations, reduces the need for invasive tests, and decreases the delay in moving from assessment to treatment. Ultimately for the patient, this leads to cheaper, faster and more accurate diagnosis, more reliable prognosis, reduction of side effects from diagnostic tests, and better-targeted treatment and care. Economically, it stimulates industry to develop and market new drugs and devices for assessment and treatment. This in turn reduces the burden on the healthcare system by prolonging independent good-quality life.
Publications
Ianus A
(2020)
Higher-order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer.
in Magnetic resonance in medicine
Drobnjak I
(2016)
PGSE, OGSE, and sensitivity to axon diameter in diffusion MRI: Insight from a simulation study.
in Magnetic resonance in medicine
Powell E
(2023)
Blood-brain barrier water exchange measurements using FEXI: Impact of modeling paradigm and relaxation time effects
in Magnetic Resonance in Medicine
Ianus A
(2017)
Double oscillating diffusion encoding and sensitivity to microscopic anisotropy.
in Magnetic resonance in medicine
Battiston M
(2018)
An optimized framework for quantitative magnetization transfer imaging of the cervical spinal cord in vivo.
in Magnetic resonance in medicine
Slator PJ
(2018)
Placenta microstructure and microcirculation imaging with diffusion MRI.
in Magnetic resonance in medicine
Shemesh N
(2016)
Conventions and nomenclature for double diffusion encoding NMR and MRI.
in Magnetic resonance in medicine
Grussu F
(2019)
Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord.
in Magnetic resonance in medicine
Battiston M
(2018)
Fast and reproducible in vivo T1 mapping of the human cervical spinal cord.
in Magnetic resonance in medicine
Gyori NG
(2022)
Training data distribution significantly impacts the estimation of tissue microstructure with machine learning.
in Magnetic resonance in medicine
Jaubert O
(2021)
Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI.
in Magnetic resonance in medicine
Zuluaga MA
(2015)
Voxelwise atlas rating for computer assisted diagnosis: Application to congenital heart diseases of the great arteries.
in Medical image analysis
Pichat J
(2018)
A Survey of Methods for 3D Histology Reconstruction.
in Medical image analysis
Lin H
(2023)
Low-field magnetic resonance image enhancement via stochastic image quality transfer
in Medical Image Analysis
Atzeni A
(2022)
Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length.
in Medical image analysis
Hawkes DJ
(2016)
From clinical imaging and computational models to personalised medicine and image guided interventions.
in Medical image analysis
Mertzanidou T
(2017)
3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging.
in Medical physics
Da Silva FTF
(2022)
Inter-sectoral prioritization of climate technologies: insights from a Technology Needs Assessment for mitigation in Brazil.
in Mitigation and adaptation strategies for global change
Tur C
(2020)
A multi-shell multi-tissue diffusion study of brain connectivity in early multiple sclerosis.
in Multiple sclerosis (Houndmills, Basingstoke, England)
Collorone S
(2019)
Reduced neurite density in the brain and cervical spinal cord in relapsing-remitting multiple sclerosis: A NODDI study
in Multiple Sclerosis Journal
Maier-Hein L
(2017)
Surgical data science for next-generation interventions.
in Nature biomedical engineering
Young AL
(2018)
Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.
in Nature communications
O'Connor JP
(2017)
Imaging biomarker roadmap for cancer studies.
in Nature reviews. Clinical oncology
Slattery CF
(2017)
ApoE influences regional white-matter axonal density loss in Alzheimer's disease.
in Neurobiology of aging
Fiford CM
(2018)
Patterns of progressive atrophy vary with age in Alzheimer's disease patients.
in Neurobiology of aging
| Description | This is a platform grant used to support a wide range of projects mostly led by early career researchers. Here are specific findings/outputs that have emerged from those projects so far, which I will add to as the project continues. Enrico Kaden: development of Spherical Mean Technique (Kaden et al MRM 2016, NIMG 2016) and release of open-source software: https://ekaden.github.io. Laura Panagiotaki: development of 3D mold for aligned in-vivo and post-operative imaging of prostate MR and histology (Bailey et al Frontiers in Oncology 2017). Marco Palombo: development of SANDI (Palombo NIMG 2020) Alexandra Young: development of the SuStaIn algorithm (Young Nature Comms 2018) Arman Eshaghi: MS subtypes discovered by SuStaIn (Eshaghi Nature Comms 2021) |
| Exploitation Route | Many different ways as it underpins a diverse range of activity. As an overall summary, delivering medical imaging technology to clinical research and practice. Primary application areas are neurology and cancer, but also made contributions in respiratory imaging, cardiac imaging, and ophthalmology. We are continuing to allocate resource within the grant using the same strategy and exploring some more adventurous allocation, which we will report back on at later stages. |
| Sectors | Healthcare,Pharmaceuticals and Medical Biotechnology |
| Description | A multi-modality, surgical planning and guidance system to improve the up-take of laparoscopic liver resection |
| Amount | £1,444,811 (GBP) |
| Funding ID | II-LA-1116-20005 |
| Organisation | National Institute for Health Research |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 12/2020 |
| Description | AI-powered brain microstructure imaging |
| Amount | £1,076,148 (GBP) |
| Funding ID | MR/T020296/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2020 |
| End | 06/2024 |
| Description | Accelerated Magnetic Resonance Imaging for Alzheimer's Disease (ADMIRA) |
| Amount | £390,000 (GBP) |
| Organisation | Alzheimer's Society |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 01/2023 |
| End | 12/2025 |
| Description | Actuated Robotic Imaging Skins |
| Amount | £2,780,000 (GBP) |
| Organisation | Royal Academy of Engineering |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 10/2019 |
| End | 09/2029 |
| Description | Assessing Placental Structure and Function by Unified Fluid Mechanical Modelling and in-vivo MRI |
| Amount | £1,124,021 (GBP) |
| Funding ID | EP/V034537/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2021 |
| End | 07/2024 |
| Description | Computational Modelling and Inference of Neurodegenerative Disease Propagation |
| Amount | £1,465,345 (GBP) |
| Funding ID | 221915 |
| Organisation | Wellcome Trust |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 11/2021 |
| End | 10/2026 |
| Description | Delineating impact of COVID-19 infection in high-risk populations |
| Amount | $155,800 (USD) |
| Organisation | Microsoft Research |
| Sector | Private |
| Country | Global |
| Start | 08/2020 |
| End | 08/2021 |
| Description | Developing single cell resolution 3D models of immune surveillance in cancer |
| Amount | £165,263 (GBP) |
| Funding ID | NS/A000069/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 12/2020 |
| Description | Developing single-cell resolution 3D models of immune surveillance in cancer |
| Amount | £487,000 (GBP) |
| Funding ID | NS/A000069/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2018 |
| End | 12/2020 |
| Description | EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health) |
| Amount | £6,034,274 (GBP) |
| Funding ID | EP/S021930/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2019 |
| End | 03/2028 |
| Description | EPSRC Doctoral Prize |
| Amount | £110,000 (GBP) |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2015 |
| End | 12/2017 |
| Description | EPSRC Doctoral Prize Fellowship |
| Amount | £100,000 (GBP) |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 09/2016 |
| End | 09/2018 |
| Description | EPSRC Early career fellowship |
| Amount | £1,000,000 (GBP) |
| Funding ID | EP/N021967/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2016 |
| End | 06/2021 |
| Description | Early Career Fellowship |
| Amount | £1,239,250 (GBP) |
| Funding ID | EP/P012841/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2017 |
| End | 03/2022 |
| Description | Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer |
| Amount | £1,035,545 (GBP) |
| Funding ID | EP/R014019/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2018 |
| End | 01/2021 |
| Description | Enabling clinical decisions from low-power MRI in developing nations through image quality transfer |
| Amount | £1,020,000 (GBP) |
| Funding ID | EP/R014019/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 02/2018 |
| End | 01/2021 |
| Description | FP7-PEOPLE, Marie Curie Action, Intra-European Fellowship |
| Amount | € 231,283 (EUR) |
| Funding ID | 627025 |
| Organisation | European Commission |
| Sector | Public |
| Country | European Union (EU) |
| Start | 01/2015 |
| End | 02/2017 |
| Description | Gold-standard assessment of prostate cancer MRI accuracy |
| Amount | $25,000 (AUD) |
| Organisation | Sydney Catalyst |
| Sector | Charity/Non Profit |
| Country | Australia |
| Start | 06/2017 |
| End | 05/2018 |
| Description | I-AIM: Individualised Artificial Intelligence for Medicine |
| Amount | £838,376 (GBP) |
| Funding ID | MR/S03546X/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2020 |
| End | 12/2023 |
| Description | IGT Network+ |
| Amount | £50,000 (GBP) |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2017 |
| End | 01/2018 |
| Description | Image Quality Transfer |
| Amount | £60,000 (GBP) |
| Organisation | Microsoft Research |
| Sector | Private |
| Country | Global |
| Start | 10/2015 |
| End | 09/2018 |
| Description | JPND: Early-Detection of Alzheimer's Disease Subtypes |
| Amount | € 1,800,000 (EUR) |
| Funding ID | MR/T046422/1 |
| Organisation | JPND Research |
| Sector | Academic/University |
| Country | Global |
| Start | 11/2020 |
| End | 11/2023 |
| Description | JPND: Stratification of presymptomatic amyotrophic lateral sclerosis: the development of novel imaging biomarkers |
| Amount | € 1,600,000 (EUR) |
| Funding ID | MR/T046473/1 |
| Organisation | JPND Research |
| Sector | Academic/University |
| Country | Global |
| Start | 07/2020 |
| End | 07/2023 |
| Description | Learning MRI and histology image mappings for cancer diagnosis and prognosis |
| Amount | £774,000 (GBP) |
| Funding ID | EP/R006032/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 12/2017 |
| End | 01/2020 |
| Description | LonDownsPREVENT: A longitudinal study of the mechanisms of cerebral amyloid angiopathy and neurodegeneration in Down syndrome to inform AD prevention |
| Amount | £1,015,308 (GBP) |
| Funding ID | MR/S011277/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2019 |
| End | 04/2023 |
| Description | MedCity Collaborate to Innovate |
| Amount | £100,000 (GBP) |
| Organisation | MedCity |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2017 |
| End | 04/2018 |
| Description | Network Pump-Priming/Equipment Grant |
| Amount | £2,200 (GBP) |
| Organisation | Alzheimer's Research UK |
| Sector | Charity/Non Profit |
| Country | United Kingdom |
| Start | 06/2017 |
| End | 06/2018 |
| Description | Real-Time Ultrasound Guided Abdominal Interventions Without a Tracking Device |
| Amount | £1,015,268 (GBP) |
| Funding ID | EP/T029404/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 06/2020 |
| End | 06/2023 |
| Description | Social Science Plus |
| Amount | £4,000 (GBP) |
| Organisation | University College London |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 02/2017 |
| End | 07/2017 |
| Description | UCL Knowledge and Innovation fund |
| Amount | £15,000 (GBP) |
| Organisation | UCL Business |
| Sector | Private |
| Country | United Kingdom |
| Start | 03/2017 |
| End | 10/2017 |
| Title | NETPAGE: NETwork Propagation-based Assessment of Genetic Events |
| Description | Statistical analysis of rare variation is challening, current rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. NETwork Propagation-based Assessment of Genetic Events (NETPAGE) is an integrative approach to investigate the biological pathways through which rare variation results in complex disease phenotypes. It can be applied to binary (i.e., case-control) studies but also to analyze quantiative traits. NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through gene interaction networks. The result of network propagation is a set of smoothed gene scores used to predict disease status or quantiative traits through sparse regression. NETPAGE is described in detail in Marzia's PLOS Computational Biology paper 'Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities'. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Too soon. None yet. |
| URL | https://github.com/maffleur/NETPAGE/releases/tag/v1.0 |
| Description | Collaboration with University of Toronto - Funded by ESRC "Self-guided Microrobotics for Automated Brain Dissection" |
| Organisation | University of Toronto |
| Country | Canada |
| Sector | Academic/University |
| PI Contribution | We are developing AI systems to study microscopic images and drive micro-scale robot control for cell harvesting |
| Collaborator Contribution | UoT develop the optical system and the harvesting robot capabilities. They are also leading the clinical data management and translational pathway. |
| Impact | Multi-disciplinary between computer science, engineering, chemisty and neuroscience. |
| Start Year | 2020 |
| Description | Maximizing the quality of life of lung cancer survivors: conventional radiotherapy versus proton therapy |
| Organisation | University of Texas |
| Department | M. D. Anderson Cancer Center |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Agreement to share methodologies for future studies. |
| Collaborator Contribution | Agreement to share data for future studies. |
| Impact | Outputs: invited seminar; blog post. This collaboration is multidisciplinary, involving computer scientists, physicists and radiation oncologists. |
| Start Year | 2017 |
| Title | MISST - Microstructure Imaging Sequence Simulation ToolBox |
| Description | Microstructure Imaging Sequence Simulation Toolbox (MISST) is a practical diffusion MRI simulator for development, testing, and optimisation of novel MR pulse sequences for microstructure imaging. MISST is based on a matrix method approach and simulates the signal for a large variety of pulse sequences and tissue models. Its key purpose is to provide a deep understanding of the restricted diffusion MRI signal for a wide range of realistic, fully flexible scanner acquisition protocols, in practical computational time. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Open Source License? | Yes |
| Impact | MISST has been used in several research studies presented at recent conferences. |
| Title | Spherical Mean Technique |
| Description | It implements a model-based imaging technique for deriving microstructural maps from MRI. |
| Type Of Technology | Software |
| Year Produced | 2016 |
| Open Source License? | Yes |
| Impact | It's quite new, but already being used in a range of clinical studies. Impact will come and we'll report back when it does. |
| URL | https://ekaden.github.io |
| Company Name | QUEEN SQUARE ANALYTICS LIMITED |
| Description | Current focus in on facilitating imaging trials in multiple sclerosis. Specifically it uses disease subtypes from the SuStaIn algorithm (Young et al Nature Comms) derived from large MS patient data sets (Eshaghi et al Nature Comms 2021) for patient stratification which helps to predict treatment response. |
| Year Established | 2020 |
| Impact | Not yet |
| Website | https://www.queensquareanalytics.com |
| Description | Blog post: Visit to MD Anderson Cancer Center, Houston, US |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Postgraduate students |
| Results and Impact | Blog post detailing the outcomes of a visit to an international clinical partner to discuss collaboration opportunities. |
| Year(s) Of Engagement Activity | 2017 |
| URL | http://catveigablog.wordpress.com/2017/10/09/ucl-global-engagement-funds-201718-visit-to-md-anderson... |
| Description | Centre for Medical Image Computing online open day |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | We organised an online open day for the research centre I worked in: UCL's Centre for Medical Image Computing (CMIC). At this open day, there were pre-recorded presentations to watch on our research, focusing on the translational aspect of the Centre's research, including our epilepsy surgery work (as in link below). This video is still available now, and was recently tweeted about from CMIC's twitter account. This open day was attended by collaborators, academics, clinicians, funding body representatives, alumni, and potential new staff. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.youtube.com/watch?v=Xnn5LSYScxg&list=PLQcRhEFbPrx4fb6jiOaaVlzE36TUGC1CR&index=3 |
| Description | Filmed for Stand Up To Cancer Campaign (for dissemination on social media) |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | I was filmed for stand up to cancer's online campaign to publicize the research they fund - I was involved in a short 'sketch' and a ~10 min interview, the interview has not yet been made available (but I have been told that it will some time) |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.facebook.com/standuptocancerUK/videos/1492355964205315/?q=stand%20up%20to%20cancer%20uk |
| Description | Institute Of Physics Annual Meeting |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | Online talk at the annual symposium of the institue of Physics |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.iopconferences.org/iop/frontend/reg/thome.csp?pageID=964207&eventID=1519&CSPCHD=00000100... |
| Description | Medical Image Computing Summer School |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Presentation at the UCL CMIC Medical Image Computing Summer School |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://medicss.cs.ucl.ac.uk/programme-2020/ |
| Description | Organisation of Early Career Workshop |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Postgraduate students |
| Results and Impact | The goal of the UCL Centre for Medical Image Computing (CMIC) Early Career workshop is to provide to early career researchers a clear and broad view on different possible career paths available to graduates. Our goal was to deliver a successful workshop where participants feel comfortable discussing their long-term goals, expectations and concerns, while hearing from peers that followed similar and different paths. The workshop was targeted at post-doctoral researchers and final year PhD students. A total of 35 participants participated in this one-day event, which took place on the 8th November 2018. We have invited UCL/CMIC alumni and collaborators from a mix of background and career stages. These include academic (research and teaching), large industries, small and medium companies, start-ups, governmental and public sector careers. The event gathered 6 speakers and other 11 mentors. The event was divided into two types of session: plenary talks and small group discussions. For the small group discussions, the participants and mentors were divided in groups to discuss different career paths (academic, industry and other) in a ratio 3:1 for participants and mentors. We have received positive feedback from attendees and invitees, that found the event very useful and appreciated the opportunity both to stop and thinking about their career paths and to network with CMIC researchers, alumni and collaborators. |
| Year(s) Of Engagement Activity | 2018 |
| Description | Organisation of Workshop on Digital Histology Reconstruction |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | The workshop was one day long and included prestigious speakers from UK, Europe and the US; mostly from academia, but also from industry. It also served to increase the visibility of newly appointed early stage lecturers of CMIC, such as Dr. Laura Panagiotaki and myself. The workshop was also a great training event, and had 40 registered attendants from CMIC. It also served to solidify the interest group in histology/MRI started set up by myself, and has been the starting point for new collaborations, e.g., with Prof. Karla Miller from Oxford (who's now helping us with MR acquisition), or Dr. Alain Pitiot (I've served as an examiner in the viva voce examination of one of his students). I am also drafting a perspective article that I'll submit in collaboration with the rest of the speakers. In addition, there's a number of grants and publications that are associated with this workshop, and have benefitted from it indirectly: CRUK/EPSRC. "Developing single-cell resolution 3D models of immune surveillance in cancer". NS/A000069/1. 1/1/2018-31/12/2020. Total: £487K. PI: M. Jansen, CoPIs: Y. Yuan, D. Treanor (Warwick), D. Alexa EPSRC. "Learning MRI and histology image mappings for cancer diagnosis and prognosis". EP/R006032/1. 1/12/2017-30/1/2020. Total: £774K. PI: D. Alexander, CoPIs: S. Punwani, L. Panagiotaki, I. Kokkinos, D. Hawkes, A. Freeman, T. Mertzanidou. J.E. Iglesias, M. Modat, L. Peter, A. Stevens, R. Annunziata, T. Vercauteren, E. Lein, B. Fischl, S. Ourselin: "Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections", under review. J. Pichat, J.E. Iglesias, S. Nousias, T. Yousry, S. Ourselin, M. Modat: "Part-to-whole Registration of Histology and MRI using Shape Elements", ICCV Bioimage Computing Workshop, 107-115 |
| Year(s) Of Engagement Activity | 2017 |
| URL | https://www.eventbrite.com/e/workshop-on-digital-histology-reconstruction-tickets-34512655281 |
| Description | The TADPOLE Challenge |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | We organised a community challenge to predict future progression of ageing subjects to Alzheimer's disease. The TADPOLE challenge: https://tadpole.grand-challenge.org. The proposals should identify what gaps the BRC could fill to realise the proposed strategy. We obtained funding from three charities: Alzheimer's Association, Alzheimer's Society, and Alzheimer's Research UK. Each provided £10K to use as prizes. We offered various categories of prizes to different groups including full-time researchers, undergraduate teams, high-school teams. The endeavour was reported in the scientific press: http://www.alzforum.org/news/community-news/tadpole-challenge-seeks-best-predictors-alzheimers. And very high profile e.g. obtaining large numbers of views on all the broadcasts, see e.g. https://www.youtube.com/watch?v=mZj-sYm7pXg&feature=youtu.be. It did a lot to raise awareness of the challenges in AD and how computer science, statistics, etc, can help, especially among school kids and their teachers. |
| Year(s) Of Engagement Activity | 2017,2018 |
| URL | https://tadpole.grand-challenge.org |
| Description | Workshop on diffusion MRI meets diffusion MRS. Combining DW-MRI and DW-MRS: a multi-scale approach to microstructure imaging |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | Title: Diffusion MRI meets diffusion MRS. Combining DW-MRI and DW-MRS: a multi-scale approach to microstructure imaging Organizers: Marco Palombo and Hui Zhang Topic: development of new methods for brain microstructure non-invasive imaging Aim: to create a stimulating forum where experts in diffusion-weighted MRI (DW-MRI) and spectroscopy (DW-MRS) techniques can discuss the best way to combine two techniques of complementary scale to improve the non-invasive brain tissue microstructure characterization. To scope out the key research challenges and opportunities associated with this multi-scale approach to microstructure imaging. Motivation: Up to now, diffusion-weighted magnetic resonance community has been working primarily on the development of either DW-MRI or DW-MRS techniques separately, without exploiting the complementarity between them to create a new unified technique which can perform much better than both on their own. We want to push the community to change its point of view, considering combining the two techniques of complementary scale to improve the non-invasive brain tissue microstructure characterization. Key invitees: Prof. F Barkhof (ION/UCH, UK); Dr. S Bisdas (ION/UCH, UK); Dr. S Punwani (ION/UCH, UK); Prof. S. Lehéricy (ICM, France); Dr. F Branzoli (ICM, France); Prof. I Ronen (LUMC, Netherlands); Dr. M Nilsson (Lund University, Sweden); Dr. J Valette (CEA/MIRCen, France). Attendees: over 50 students, researchers and professors from UCL; KCL; Cambridge University; Oxford University; Imperial College; Crick's Institute; NHS. Outcomes: 1) Review paper on the combination of DW-MRI and DW-MRS, in collaboration with Julien Valette (CEA Fontenay-aux-Roses in Paris, France), Itamar Ronen (LUMC in Leiden, the Netherlands) and Noam Shemesh (Champalimaud Centre for the Unknown in Lisbon, Portugal), published on NeuroImage 2017 (https://doi.org/10.1016/j.neuroimage.2017.11.028); 2) Collaboration with Francesca Branzoli and Stephane Lehericy (ICM in Paris, France): two abstracts submitted to ISMRM 2018; two papers in preparation; consolidation of long-term collaboration. 3) Collaboration with Noam Shemesh (Champalimaud Centre for the Unknown in Lisbon, Portugal): two abstracts submitted at the ISMRM 2018; two papers in preparation; establishment of new long-term collaboration. 4) Invited speaker at an equivalent workshop on combining DW-MRI and DW-MRS, 10-12 October 2018, in ICM in Paris, France, organized by Julien Valette, Itamar Ronen and Francesca Branzoli. The aim of the meeting will be to consolidate the collaborations established in the UCL's workshop and to discuss future projects. |
| Year(s) Of Engagement Activity | 2017 |
| Description | online interview for Bloomsbury festival 2020 |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | online interview with the Radiotherapy Image Computing (RTIC) group for the Bloomsbury festival 2020, broadcast live on the festival Facebook page and available after the event on facebook and youtube |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.youtube.com/watch?v=JSdWRM2XBJs&t=1551s |
| Description | organiser of WBIR 2020 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | I was co-organiser of the WBIR 2020 conference, held online in December 2020 |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://wbir2020.org/ |
| Description | stand up to cancer live |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | our research featured on the stand up to cancer live telethon, broadcast on channel 4 in October 2018 - this was at primetime on a Friday night, so may have been seen by millions of TV viewers. I provided input and advice on the segment to help ensure the science was presented in an accurate but easily understandable way |
| Year(s) Of Engagement Activity | 2018 |