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Wellcome / EPSRC Centre for Interventional and Surgical Sciences

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Deep Learning for Automated Registration in Laparoscopic Liver Surgery

Liver cancer is a major global health problem affecting an estimated 1.4 million people every year (2012). Surgery is the main curative option. Despite the well-known advantages of keyhole surgery only a small percentage (5-30%) can be given this choice, due to the increased difficulty and associated risks, relative to open surgery.

Image-guidance systems, including augmented reality have been proposed as a way to assist the surgeon and to reduce the level of risk thereby allowing a higher percentage of patients to benefit from keyhole surgery. 

CMIC has developed a keyhole surgery system that can display information from pre-operative scans such as Computed Tomography (CT) along with the live video image. The proposed CDT project will extend the existing system by developing novel learning algorithms for tissue classification, tool identification, tool tracking or real-time registration. 

Our aim is to produce the first clinically viable, accurate and easy to use system. This would impact patients undergoing liver surgery, but in addition, the technology would also be broadly applicable to clinical procedures on the pancreas, kidneys and gall bladder. 

The project fits within the EPSRC early stage of algorithm development (TRL 1-3), as it would entail first proof of concept of the basic science, developing the core learning algorithms. However, the proposal would additionally benefit from the wider programme of work in WEISS and CMIC, which includes an NIHR i4i Product Development Award, enabling data collection from patient cases.

Supervisor

Dr Matt Clarkson