Longitudinal Prostate MR Image Registration for Patients in Active Surveillance

1) Brief description of the context of the research including potential impact

Prostate cancer is one of the most diagnosed cancer in male in many parts of the western world. As
one of the most common observation strategies, active surveillance is used to help monitor the low
risky prostate cancer patients on the progression of the tumor and to decide the time point to receive
the treatment. Challenges come from the uncertainties on accept criterion, decision making, determine
the time interval for follow-up visits and the short- and long-term outcomes of the patients. With the
increasing number of patients who were added into active surveillance project in recent years, deep
learning technologies are considered to use for helping the doctors face these challenges. Which may
both relief the doctors form laborious medical image interpreting works and help make better
decisions.

2) Aims and Objectives

-The specific objectives are to:
1. Perform a retrospective study on an active surveillance project to build a deep learning based
model for helping the doctors observe the progression condition of the tumors and make treatment
strategies or decisions.
2. Exploring the method of longitudinal prostate image registration, which is a principle method in
this project. The project drives the explorations including but not limited to, for example, the
research on the influence of distribution of the inter-/intra-patient and single/multi-modality MR
image registration.

3) Novelty of Research Methodology

The method is concentrating on the registration of the longitudinal magnetic resonance images with
deep learning in a weakly supervised manner, which could learn the morphological changes of the
images in a sequence of follow-up visits. The morphological changes could be quantified and be used
as a factor for discriminate the progression status of the tumor, which could be used in other downstreaming
tasks.

4) Alignment to EPSRC's strategies and research areas

Healthcare Technologies: Artificial Intelligence Technologies / Medical Imaging

5) Any companies or collaborators involved

None.

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

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