Abstract
In this article, we present a method for empty guiding catheter segmentation in fluoroscopic X-ray images. The guiding catheter, being a commonly visible landmark, its segmentation is an important and a difficult brick for Percutaneous Coronary Intervention (PCI) procedure modeling. In number of clinical situations, the catheter is empty and appears as a low contrasted structure with two parallel and partially disconnected edges. To segment it, we work on the level-set scale-space of image, the min tree, to extract curve blobs. We then propose a novel structural scale-space, a hierarchy built on these curve blobs. The deep connected component, i.e. the cluster of curve blobs on this hierarchy, that maximizes the likelihood to be an empty catheter is retained as final segmentation. We evaluate the performance of the algorithm on a database of 1250 fluoroscopic images from 6 patients. As a result, we obtain very good qualitative and quantitative segmentation performance, with mean precision and recall of 80.48 and 63.04% respectively. We develop a novel structural scale-space to segment a structured object, the empty catheter, in challenging situations where the information content is very sparse in the images. Fully-automatic empty catheter segmentation in X-ray fluoroscopic images is an important and preliminary step in PCI procedure modeling, as it aids in tagging the arrival and removal location of other interventional tools.
Highlights
In interventional cardiology, Percutaneous Coronary Intervention (PCI) procedures are performed with real time streaming of X-ray images, most of it being low dose X-ray images called fluoroscopic images
Various works on segmentation of pigtail catheters [10] and EP catheters [12, 13] portray the importance of segmentation of interventional tools and endovascular devices
Dataset: We evaluate our empty catheter segmentation algorithm using a dataset of 1250 fluoroscopic images
Summary
Percutaneous Coronary Intervention (PCI) procedures are performed with real time streaming of X-ray images, most of it being low dose X-ray images called fluoroscopic images. We aim at designing a family of image processing algorithms to identify the presence of different interventional tools in the images and link this information to high-level knowledge describing the steps of the procedure and the user expectations for each of them This is a form of semantic analysis which is fundamentally different from the traditional automatic X-ray exposure control combining user interactions and measure of the statistics of the image. Monitoring the interventional tools like guiding catheter, EP catheter, guide wire tip, guide wire body, marker balls, balloon, stent is necessary to obtain this information Segmentation of these tools is a fundamental brick in such semantic analysis. Various works on segmentation of pigtail catheters [10] and EP catheters [12, 13] portray the importance of segmentation of interventional tools and endovascular devices
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