This project focuses on the development of a learning-based system capable of localizing anatomy in X-Ray fluoroscopy video streams acquired during mechanical thrombectomy (MT). Mechanical thrombectomy are emergency procedures for acute ischaemic stroke. An endovascular catheter is navigated from the groin up to the brain under real-time X-ray fluoroscopy guidance. MT is challenging to perform in part due to the complexity of fluoroscopy image interpretation. Computer vision approaches have shown promising capabilities in surgical scene understanding across several minimally invasive surgical specialties but their development in interventional neuroradiology remains at its infancy. The PhD candidate will develop novel methods to automatically identify anatomical structures in fluoroscopy videos with the ambition of providing a better spatial understanding to the interventional neuroradiologist (INR). This project will require close collaboration with expert INRs to build annotated fluoroscopy databases and validate the performance of the proposed solutions.
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Anatomy localisation in X-ray fluoroscopy videos for mechnical thrombectomy
