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UCL EPSRC Centre for Doctoral Training in Intelligent Integrated Imaging in Healthcare

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NOW CLOSED :Measuring the variability of AI and manual delineations for radiotherapy

4-year PhD studentship funded by NPL, Mount Vernon Cancer Centre and the i4health CDT - Deadline 18th December 2022

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16 November 2022

Primary Supervisor:   Jamie McClelland
Additional Supervisors:   Catharine Clark (UCL/UCLH/NPL/RTTQA), Nadia Smith (NPL), Elizabeth Miles (RTTQA, MVCC), Peter Hoskin (RTTQA, MVCC).

A 4 year funded PhD studentship is available in a partnership between the Centre for Medical Image Computing (CMIC) and the Wellcome / EPSRC centre for Interventional and Surgical Sciences (WEISS) at UCL, the Medical Physics and Data Science groups at the National Physical Laboratory (NPL), the Mount Vernon Cancer Centre, and the NIHR Radiotherapy Trials Quality Assurance (RTTQA) group. Funding will be at least the UCL minimum. Stipend details can be found here.
The successful candidate will join the UCL CDT in Intelligent, Integrated Imaging in Healthcare (i4health) cohort and benefit from the activities and events organised by the centre. They will also benefit from activities and events organised by CMIC, WEISS, NPL, and RTTQA, and wider interaction with these groups.

Funding 

A full studentship is available for Home fee applicants.

Overseas fee payers will be considered but will be required to either:

  1. prove that they have secured a separate scholarship to cover the fee difference between Home Fee and the Overseas fee (Self-funding the difference will not be considered)

OR

  1. Apply through the UCL Research Excellence Scholarship (RES) for an Overseas Fee Scholarship.

Overseas fee payers must contact Dr Jamie McClelland (j.mcclelland@ucl.ac.uk) before submitting their application to discuss eligibility.

UCL’s fee eligibility criteria can a be found by following this link.

Project Background: 

Variability and reproducibility in delineating anatomy is arguably the biggest source of uncertainty in radiotherapy and inaccuracies impact on both tumour control and normal tissue toxicity. More accurate delineations would improve outcomes for patients both in survival and reduced toxicity, with the greatest impact for proton therapy where delineation variability can create large dosimetric uncertainties. Typically, delineation is performed manually by a clinician following national and local guidelines, which can take a significant amount of time and result in considerable variability between patients, clinicians, and clinical sites. Recently, several commercial and research AI solutions to delineate tissues have become available, with more on the horizon, which have the potential to both save time and reduce variability compared to manual outlining. These solutions are now starting to be adopted clinically in some hospitals, and the next few years are likely to see a considerable increase in the use of AI-generated delineations in clinical trials and for routine clinical practice. It is therefore imperative that objective and efficient methods for fairly assessing and comparing manual and AI-generated delineations are developed. This will help improve the efficiency and effectiveness of clinical trials and facilitate the safe adoption of AI solutions into clinical practice.

Research aims: 

To develop state-of-the-art machine learning methods for measuring and parameterising variability in delineations over many patients, being suitable for both manual and AI-generated delineations. These methods will be used for objective and balanced comparisons between manual and AI-generated delineations for different cohorts of patients. This will be implemented to critically evaluate delineations for specific patients and determine if they are within the range of variability seen in the wider population, facilitating fair and efficient QA of delineations for clinical trials and routine clinical use.

Person specification & requirements:

Applicants are generally expected to have, or be predicted to obtain, at least a First Class Honours or Upper Second Class Honours UK Bachelor’s degree in a suitable subject such as Computer Science, Engineering, or Physics. This project requires strong programming skills and previous experience with image analysis and machine learning. Previous experience or a strong interest in radiotherapy and/or medical image analysis is highly desirable.

Application Deadline :Friday 18th December 2022

How to apply:

Please complete the following steps to apply.

  • Send an expression of interest and current CV to: j.mcclelland@ucl.ac.uk and cdtadmin@ucl.ac.uk .Please quote Project Code:  23009 in the email subject line.
  • Make a formal application to via the UCL application portal
  • Please select the programme code Medical Imaging TMRMEISING01 and enter Measuring the variability of AI and manual delineations for radiotherapy using machine learning, Project Code 23009 under ‘Name of Award 1’