At the COMPuter ASsisted Surgery (COMPASS) group we produce image-guidance systems, by developing novel and clinically validated algorithms so that patients may benefit from safer and more accurate surgical procedures.
Our team is involved in a range of projects, making an impact on challenges facing clinicians in abdominal and neurosurgical surgery and therapy.
Liver cancer is a major global health problem, affected an estimated 1.4 million people a year. We are developing a computer assisted laparoscopic surgery system to guide the surgeon, thereby reducing the level of risk. Our vision is that in the future, all patients requiring liver surgery will be offered keyhole surgery, resulting in a safer, quicker procedure with faster recovery times.
A key challenge for researchers in computational science is to write robust and reliable software with reduced maintenance costs. The Surgical NAvigation Platform with PYthon (SNAPPY) integrates devices such as trackers and various imaging sources and provides a framework for researchers and Research Software Engineers to work together and deliver code to the clinic in record time.
Ultrasound (US) image-guidance during surgery can improve the likelihood of a successful clinical outcome by presenting the clinician with real-time information process. We are developing a new framework for real-time registration that can be applied to laparoscopic surgery, endoscopic fetal surgery, robot-assisted surgery and image-guided ablation of tumours.
VEROnA image analysis project focusses on the information provided by imaging of vasculature embolised with radiopaque micro-spheres. This involves registration of imaging across multiple scales and modalities including CT, perfusion CT, DCE-MRI and micro-CT of resected samples.
The purpose of this project is to develop a three-dimensional surgical navigation system to combine images from different scans to help surgeons visualise the area in much greater detail and make skull base surgery safer and more effective.
The integrated navigation system will combine magnetic resonance imaging of the brain and facial nerve with ultrasound images taken during the operation.
ML Epilepsy Surgey
A proportion of people affected by refractory focal epilepsy respond absolutely and unequivocally to epilepsy surgery. We are developing text mining and machine learning methods to detect which patients will become seizure free, and hence inform clinical decision making.