User-constrained guidewire localization in fluoroscopy

In this paper we present a learning-based guidewire localization algorithm which can be constrained by user inputs. The proposed algorithm automatically localizes guidewires in fluoroscopic images. In cases where the results are not satisfactory, the user can provide input to constrain the algorithm by clicking on the guidewire segment missed by the detection algorithm. The algorithm then re-localizes the guidewire and updates the result in less than 0.3 second. In extreme cases, more constraints can be provided until a satisfactory result is reached. The proposed algorithm can not only serve as an efficient initialization tool for guidewire tracking, it can also serve as an efficient annotation tool, either for cardiologists to mark the guidewire, or to build up a labeled database for evaluation. Through the improvement of the initialization of guidewire tracking, it also helps to improve the visibility of the guidewire during interventional procedures. Our study shows that even highly complicated guidewires can mostly be localized within 5 seconds by less than 6 clicks.