A Coordinate-Regression-Based Deep Learning Model for Catheter Detection during Structural Heart Interventions

Abstract

With a growing geriatric population estimated to triple by 2050, minimally invasive procedures that are image-guided are becoming both more popular and necessary for treating a variety of diseases. To lower the learning curve for new procedures, it is necessary to develop better guidance systems and methods to analyze procedure performance. Since fluoroscopy remains the primary mode of visualizations, the ability to perform catheter tracking from fluoroscopic images is an important part of this endeavor. This paper explores the use of deep learning to perform the landmark detection of a catheter from fluoroscopic images in 3D-printed heart models. We show that a two-stage deep-convolutional-neural-network-based model architecture can provide improved performance by initially locating a region of interest before determining the coordinates of the catheter tip within the image. This model has an average error of less than 2% of the image resolution and can be performed within 4 milliseconds, allowing for its potential use for real-time intraprocedural tracking. Coordinate regression models have the advantage of directly outputting values that can be used for quantitative tracking in future applications and are easier to create ground truth values (~50× faster), as compared to semantic segmentation models that require entire masks to be made. Therefore, we believe this work has better long-term potential to be used for a broader class of cardiac devices, catheters, and guidewires.

Use of Yolo Detection for 3D Pose Tracking of Cardiac Catheters Using Bi-Plane Fluoroscopy

“…The results are shown in Table 2. As can be seen for all four classes, our method outperforms the VGG cascaded architecture proposed by Aghasizade et al [16] in both the average and standard deviation of accuracy, with respect to the 3D Euclidean distance between predictions and labels. Other than the abovementioned finding, our method is able to provide accurate prediction with an error of less than 0.3 (mm) on average for the tip, marker, and entry landmark features, and approximately 0.42 (mm) error for the bend, resulting in four distinct 3D coordinates for catheter pose detection and tracking.…”

“…Two 3D representations of the LAO90 and AP views are provided in Figure 5, with entry, bend, marker and tip presented in red, green, blue and purple bounding boxes respectively. As part of our comparative analysis, we experimented with the method proposed by Aghasizade et al [16] based on the same paired dataset. The results are shown in Table 2.…”

“…Data Collection: The catheter image data were collected in two sessions under the physician's supervision, with various operating settings in terms of handling, maneuvering, and position in experiments, and consisted of two datasets. One dataset consisted of 529 fluoroscopic samples, previously gathered [16] with a custom-made 3D-printed heart and a metal spray-painted spine to provide visibility under X-ray, gathered during a mock procedure in the catheterization lab. It should be noted that the 3D-printed heart, the metal spray-painted spine, and the catheter were located within an acrylic box, thus producing additional artifacts in the image that would not be present in clinical images.…”

“…This is more obvious in conjunction with augmented reality, image guidance systems, and MIS for cardiac interventions [15]. Two studies published in 2023 [16] and 2021 [17] focus on applying deep learning solutions to detect the position of the tip of a catheter tracked from bi-plane fluoroscopic images. Torabinia et al designed a U-Net model, trained on masks of the catheter's tip radio-opaque marker.…”

“…Following this study, ref. [16] was published that applied a deep cascaded VGG neural network to detect the tip of the catheter by first learning and locating the tip in a grid and then identifying the local and global tip coordinates [16]. The researchers stated that if incorrectly selected regions are removed, the model is able to provide a 7.36-pixel mean error.…”