Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries.

Accurate 6-DoF pose estimation of surgical instruments during minimally invasive surgeries can substantially improve treatment strategies and eventual surgical outcome. Existing deep learning methods have achieved accurate results, but they require custom approaches for each object and laborious setup and training environments often stretching to extensive simulations, whilst lacking real-time computation. We propose a general-purpose approach of data acquisition for 6-DoF pose estimation tasks in X-ray systems, a novel and general purpose YOLOv5-6D pose architecture for accurate and fast object pose estimation and a complete method for surgical screw pose estimation under acquisition geometry consideration from a monocular cone-beam X-ray image.

Visualization of myocardial perfusion derived from coronary anatomy.

Visually assessing the effect of the coronary artery anatomy on the perfusion of the heart muscle in patients with coronary artery disease remains a challenging task. We explore the feasibility of visualizing this effect on perfusion using a numerical approach. We perform a computational simulation of the way blood is perfused throughout the myocardium purely based on information from a three-dimensional anatomical tomographic scan.

CoViCAD: comprehensive visualization of coronary artery disease.

We present novel, comprehensive visualization techniques for the diagnosis of patients with Coronary Artery Disease using segmented cardiac MRI data. We extent an accepted medical visualization technique called the bull's eye plot by removing discontinuities, preserving the volumetric nature of the left ventricular wall and adding anatomical context. The resulting volumetric bull's eye plot can be used for the assessment of transmurality.