Abstract
Mitral regurgitation (MR) is a condition caused by a deformity in the mitral valve leading to the backflow of blood into the left atrium. MR can be treated through a minimally invasive procedure and our lab is currently developing a robot that could potentially be used to treat MR. The robot would carry a clip that latches onto the valve's leaflets and closes them to minimize leakage. The robot's accurate localization is needed to navigate the clip to the leaflets successfully. This paper discusses algorithms used to track the clip's position and orientation under real-time using C-arm fluoroscopy. The positions are found through a deep learning semantic segmentation framework and the pose is found by calculating its bending and rotational angles. The robot's bending angle and the clip's rotational angle is found through an equivalent ellipse algorithm and an SVM classifier, respectively, and were validated by comparing orientations obtained from an electromagnetic tracker. The bending angle calculation has an average error of 7.7° and the rotational angle calculation is 76% for classifying them into five classes. Execution times are within 100ms and hence this could be a promising approach in real-time pose estimation.