MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Detection of electrophysiology catheters in noisy fluoroscopy images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Semi-automatic catheter reconstruction from two views
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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Electrophysiology ablation procedures are performed in an interventional lab. The therapy is delivered through several catheters introduced in cardiac chambers under x-ray guidance. They are also be used to measure some local electrical properties which can be color-coded. A kind of color-map is then established and it can be overlaid to images of the anatomy obtained with fluoroscopy. A potential improvement in the workflow of the procedure may be reached by tracking the location of the tip of the catheter performing the measurement. We propose here an image-based strategy to detect it and we report the results obtained on a large clinical database. We segment the object of interest by selecting contrasted objects and we characterize them by taking into account all possible co founding factors. A selection strategy has been defined from the distribution of the found values for the true positive and false positive elements in a first clinical database (3000 images from a single site). We got a success rate for the detection of the target object of 86% on a larger database formed of about 4500 images coming from 7 different sites. We also developed an active learning strategy for improving the performance of the algorithm and its stability in the field. The principle is to take into account the user's manual correction made on a given frame when processing the following ones, which is adapted to the clinical workflow: the segmentation result is assessed and corrected by an operator for each frame. We then gained additional 6% up to 91% on the success rate: the number of algorithm mistakes to be corrected by the operator is reduced to an acceptable level.