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Deep Machine Learning for Automated Lesion Detection in PET-CT


Project Description

This project will develop automated detection of lesions in Positron Emission Tomography (PET) using deep machine learning. This research builds on a series of successful research carried out at the host institution including the development of an award-winning automated PET segmentation algorithm (Berthon et al, IPEM Manufacturers’ Award for Innovation 2015) and the implementation of an award-winning approach to distributed learning (Deist et al ESTRO-Varian research award 2019). A large database of pre-contoured scans from retrospective datasets acquired at our centre will be used for training and validation. External validation of the developed technique will also be possible as we have a large network of external organisations collaborating with us in this field using a technique known as distributed machine learning (www.eurocat.info). In addition, our team at the School of Engineering is working with Intel Corp and Velindre Cancer Centre to deliver AI Solutions for Personalised Radiotherapy (Welsh Government “Efficiency Through Technology Programme”) and this proposed research will be linked to this exciting ongoing research activity.


Furthermore, PETIC makes use of highly specialized equipment to deliver a world class diagnostic PET service alongside academic excellence to support academia and the life science industry in Wales and beyond. We have existing collaborations with both commercial and industrial partners including the National Physics Laboratory and is currently the only facility in the UK and one of a few word-wide with the capability to produce novel radio-isotopes such as 89Zirconium. The department also regularly participates in both local and multi-centre trials and has a vast array of ongoing clinical research projects with a total secured funding for joint projects in excess of £3 million.

The student will be part of a multi-disciplinary team inclusive of consultant radiologists, radiographers, chemists, medical physicists and a group of 20 PhD students working in medical imaging, sensors and therapy. The student will be joining an internationally leading research group on a project that will push the research area forward. Our research output is regularly presented at peer-reviewed international conference and published in high impact factor academic journals.

Selected recent references from our group:
1. Berthon et al Physics in Medicine and Biology. 2016, DOI: 10.1088/0031-9155/61/13/4855
2. Hatt et al Medical physics. 2017, DOI: 10.1002/mp.12124
3. Berthon et al Medical Physics. 2017, DOI: 10.1002/mp.12312
4. Berthon et al Radiotherapy and Oncology. 2017, DOI: 10.1016/j.radonc.2016.12.008
5. Foley et al European Radiology. 2018, DOI: 10.1007/s00330-017-4973-y
6. Foley et al Radiother Oncol. 2018 doi: 10.1016/j.radonc.2018.10.033.

ELIGIBILITY

You should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.

Applicants with a Lower Second Class degree will be considered if they also have a master’s degree. Applicants with a minimum Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.

Funding Notes

Full awards, including the Tuition fee and maintenance stipend (Approx. £14,777 in 2018/19), are open to UK Nationals and EU students who can satisfy UK residency requirements. To be eligible for the full award, EU Nationals must have been in the UK for at least 3 years prior to the start of the course for which they are seeking funding, including for the purposes of full-time education.

References

Applications should be made online at: https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/engineering.

Please note the following when completing your online application:

The Programme name is Doctor of Philosophy in Engineering with an October 2019 start date.

In the "Research proposal and Funding" section of your application, please specify the project title, supervisors of the project and copy the project description in the text box provided.

Please select “No, I am not self-funding my research” when asked whether you are self-funding your research.

Please quote “project ID” when asked "Please provide the name of the funding you are applying for".

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