Skip to main content

Localised 3D mapping for risk assessment and prognosis of inflammatory bowel disease

PGR-P-1363

Coronavirus information for applicants and offer holders

We hope that by the time you’re ready to start your studies with us the situation with COVID-19 will have eased. However, please be aware, we will continue to review our courses and other elements of the student experience in response to COVID-19 and we may need to adapt our provision to ensure students remain safe. For the most up-to-date information on COVID-19, regularly visit our website, which we will continue to update as the situation changes www.leeds.ac.uk/covid19faqs

Key facts

Type of research degree
PhD
Application deadline
Friday 30 September 2022
Project start date
Sunday 1 January 2023
Country eligibility
International (open to all nationalities, including the UK)
Funding
Funded
Source of funding
University of Leeds
Additional supervisors
Dr Sharib Ali (computational endoscopic imaging/machine learning), Dr. Venkat Subramanian (consultant gastroenterologist), Prof. Andy Bulpitt (computational pathology/machine learning)
Schools
School of Computing
<h2 class="heading hide-accessible">Summary</h2>

Endoscopy is used to diagnose and monitor disease progression in hollow organs (oesophagus, stomach, colorectal, bladder and ureter, uterus, and peritoneum). Endoscopy remains highly operator dependent with concerns on quality of surveillance and variability in patient outcomes. Both effective patient treatment and minimising associated economic burden is of high importance. Diseases such as Inflammatory Bowel Disease (IBD) require patient monitoring over time and have a higher risk of developing colorectal cancer. IBD is characterised by symptomatic relapse and remission, requiring frequent endoscopic surveillance and risk monitoring through lesion and dysplasia identification. Monitoring and assessing disease progression and measuring response to drug therapy is critical in effective management of IBD. The project will aim at developing deep learning methods for - a) quantifying severity of IBD based on clinically established scoring systems and b) longitudinal comparison using localised 3D maps.

<h2 class="heading hide-accessible">Full description</h2>

<p>Endoscopy is used to diagnose and monitor disease progression in hollow organs (oesophagus, stomach, colorectal, bladder and ureter, uterus, and peritoneum). Endoscopy remains highly operator dependent with concerns on quality of surveillance and variability in patient outcomes. Both effective patient treatment and minimising associated economic burden is of high importance. Diseases such as Inflammatory Bowel Disease (IBD) require patient monitoring over time and have a higher risk of developing colorectal cancer. IBD is characterised by symptomatic relapse and remission, requiring frequent endoscopic surveillance and risk monitoring through lesion and dysplasia identification. Monitoring and assessing disease progression and measuring response to drug therapy is critical in effective management of IBD. The project will aim at developing deep learning methods for - a) quantifying severity of IBD based on clinically established scoring systems and b) longitudinal comparison using localised 3D maps. Leeds Teaching Hospitals NHS Trust is one of the major centres in colorectal cancer imaging in the UK and has also been developing several cutting-edge technologies towards robotic interventions unlike conventional endoscopy. The successful candidate will work in an exciting multi-disciplinary environment with access to large multi-modal patient data cohorts.</p> <p>The study aims at performing multi-center studies and publishing high-impact scientific and clinical papers. The work will be aimed for clinical translation by the end of the PhD. The project will be supervised by Dr. Sharib Ali (computational endoscopic imaging/machine learning), and co-supervised by Dr. Venkat Subramanian (consultant gastroenterologist) and Prof. Andy Bulpitt (computational pathology/machine learning). There will also be the possibility of working together with other national and international collaborators.</p> <p>Requirements: Applicants must be of outstanding academic merit, with (or be expected to gain) either a first class or an upper second class Honours Degree (or the international equivalent), or an MSc/MRes with distinction. Enthusiastic and self-motivated candidates with a background in either Computer Science, Engineering, Physics or Mathematics or a related discipline are encouraged to apply. Good knowledge and demonstrable experience in imaging, medical imaging, computer vision, machine learning/deep learning is advantageous. Strong skills and interest in programming using python, C/C++; using deep learning libraries such Pytorch, Tensorflow, and other computer vision libraries such as OpenCV, Open3D, Blender are a plus.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is Localised 3D mapping for risk assessment and prognosis of inflammatory bowel disease as well as Sharib Ali as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p>Applications will be considered on an ongoing basis. &nbsp;Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application. &nbsp;We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p>Please note that you must provide the following documents in support of your application by the closing date of 30 September 2022:</p> <p>&bull;&nbsp;&nbsp; &nbsp;Full Transcripts of all degree study or if in final year of study, full transcripts to date<br /> &bull;&nbsp;&nbsp; &nbsp;Personal Statement outlining your interest in the project<br /> &bull;&nbsp;&nbsp; &nbsp;CV<br /> &nbsp;</p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p>A highly competitive School of Computing Scholarship covering full Academic Fees at the UK Fee rate of &pound;4,600 or International Fee rate of &pound;25,500 together with a Maintenance grant of &pound;16,062 for academic session 2022/23 per year for 3 years.</p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<h2 class="heading">Contact details</h2>

<p>For further information about this project, please contact Professor Andy Bulpitt<br /> e: <a href="mailto:a.j.bulpitt@leeds.ac.uk">a.j.bulpitt@leeds.ac.uk</a>, t: +44 (0)113 343 5057.</p> <p>For further information about your application, please contact Doctoral College Admissions<br /> e: <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>, t: +44 (0)113 343 5057.</p>