The present invention relates to learning based object detection in medical images, and more particularly, to learning based object detection using a set of features designed to effectively detect medical devices in fluoroscopic images.
In image guided interventions, clinicians use medical devices that are inserted into patients' blood vessels to perform various operations. Various types of medical devices are used in image guide interventions, including catheters, guidewires, and stents. Such medical devices are typically visible in the fluoroscopic images during surgery and typically subject to both breathing motion and cardiac motions. Often, the visibility of medical devices in the fluoroscopic images if affected by the radiation dose, image artifacts, and occlusion by anatomic structures. It is a challenging but important task to automatically and robustly detect and track medical devices in fluoroscopic images, in order to provide quantitative information regarding the position and motion of such devices and to assist in computer aided interventional procedures.