1. A method for detecting and tracking coronary sinus (CS) catheter electrodes in a fluoroscopic image sequence comprising: initializing a catheter electrode model in a first frame of a fluoroscopic image sequence based on input locations of a plurality of CS catheter electrodes in the first frame, wherein the catheter electrode model in the first frame comprises a plurality of template points corresponding to locations of the CS catheter electrodes in the first frame; and tracking the catheter electrode model in a second frame of the fluoroscopic image sequence by: detecting electrode position candidates in a second frame of the fluoroscopic image sequence using at least one trained electrode detector; generating catheter electrode model candidates in the second frame based on the detected electrode position candidates and the catheter electrode model initialized in the first frame by translating each of the plurality of template points of the catheter electrode model initialized in the first frame to each of the detected electrode position candidates in the second frame; calculating a probability score for each of the catheter electrode model candidates in the second frame; and selecting one of the catheter electrode model candidates in the second frame based on the probability score, wherein the selected catheter electrode model candidate provides locations of the plurality of CS catheter electrodes in the second frame.
1. A method for detecting and tracking coronary sinus (CS) catheter electrodes in a fluoroscopic image sequence comprising: initializing a catheter electrode model in a first frame of a fluoroscopic image sequence based on input locations of a plurality of CS catheter electrodes in the first frame, wherein the catheter electrode model in the first frame comprises a plurality of template points corresponding to locations of the CS catheter electrodes in the first frame; and tracking the catheter electrode model in a second frame of the fluoroscopic image sequence by: detecting electrode position candidates in a second frame of the fluoroscopic image sequence using at least one trained electrode detector; generating catheter electrode model candidates in the second frame based on the detected electrode position candidates and the catheter electrode model initialized in the first frame by translating each of the plurality of template points of the catheter electrode model initialized in the first frame to each of the detected electrode position candidates in the second frame; calculating a probability score for each of the catheter electrode model candidates in the second frame; and selecting one of the catheter electrode model candidates in the second frame based on the probability score, wherein the selected catheter electrode model candidate provides locations of the plurality of CS catheter electrodes in the second frame. 9. The method of claim 1 , wherein the step of calculating a probability score for each of the catheter electrode model candidates in the second frame comprises: calculating a probability score based on a first probability score and a second probability score, wherein the first probability score is calculated by normalized cross correlation between intensity values of template points in the second frame identified by the respective catheter electrode model candidate and the intensity values of the template points in the first frame identified by the catheter electrode model, and the second probability score is calculated based on detection scores of the electrodes identified by the respective catheter electrode model candidate determined by the at least one trained electrode detector, wherein the electrodes identified by the respective catheter electrode model candidate are identified in the second frame using an electrode mask derived from the template points in the first frame.