Automatic tool segmentation and tracking during robotic intravascular catheterization for cardiac interventions.

BACKGROUND

Cardiovascular diseases resulting from aneurism, thrombosis, and atherosclerosis in the cardiovascular system are major causes of global mortality. Recent treatment methods have been based on catheterization of flexible endovascular tools with imaging guidance. While advances in robotic intravascular catheterization have led to modeling tool navigation approaches with data sensing and feedback, proper adaptation of image-based guidance for robotic navigation requires the development of sensitive segmentation and tracking models without specificity loss. Several methods have been developed to tackle non-uniform illumination, low contrast; however, presence of untargeted body organs commonly found in X-ray frames taken during angiography procedures still presents some major issues to be solved.

METHODS

In this study, a segmentation method was developed for automatic detection and tracking of guidewire pixels in X-ray angiograms. Image frames were acquired during robotic intravascular catheterization for cardiac interventions. For segmentation, multiscale enhancement filtering was applied on preprocessed X-ray angiograms, while morphological operations and filters were applied to refine the frames for pixel intensity adjustment and vesselness measurement. Minima and maxima extrema of the pixels were obtained to detect guidewire pixels in the X-ray frames. Lastly, morphological operation was applied for guidewire pixel connectivity and tracking in segmented pixels. Method validation was performed on 12 X-ray angiogram sequences which were acquired during intravascular catheterization trials in rabbits.

RESULTS

The study outcomes showed that an overall accuracy of 0.995±0.001 was achieved for segmentation. Tracking performance was characterized with displacement and orientation errors observed as 1.938±2.429 mm and 0.039±0.040°, respectively. Evaluation studies performed against 9 existing methods revealed that this proposed method provides more accurate segmentation with 0.753±0.074 area under curve. Simultaneously, high tracking accuracy of 0.995±0.001 with low displacement and orientation errors of 1.938±2.429 mm and 0.039±0.040°, respectively, were achieved. Also, the method demonstrated higher sensitivity and specificity values compared to the 9 existing methods, with a relatively faster exaction time.

CONCLUSIONS

The proposed method has the capability to enhance robotic intravascular catheterization during percutaneous coronary interventions (PCIs). Thus, interventionists can be provided with better tool tracking and visualization systems while also reducing their exposure to operational hazards during intravascular catheterization for cardiac interventions.