# Guide3D A Bi-planar X-ray Dataset for 3D Shape Reconstruction (Part 2)

We propose the **Guid3D Dataset**, a comprehensive resource specifically designed to advance 3D reconstruction and segmentation in endovascular navigation. This dataset addresses key limitations in the field, such as the scarcity of high-quality, publicly accessible datasets, by providing a diverse collection of real and synthetic imaging data. Guid3D includes detailed annotations for guidewire and catheter segmentation, alongside multi-view fluoroscopic data that supports accurate 3D modeling. By offering a standardized platform for algorithm development and evaluation, Guid3D aims to bridge the gap between research and clinical practice, facilitating improvements in precision, visualization, and tool tracking during endovascular procedures. Through this dataset, we seek to accelerate innovation in medical imaging, contributing to safer and more effective interventions.

## Data Collection Setup

**X-ray System.** Our setup employed a bi-planar X-ray system equipped with 60 kW Epsilon X-ray generators and 16-inch image intensifier tubes by Thales, featuring dual focal spot Varian X-ray tubes for high-definition imaging. The system included Ralco automatic collimators for precise alignment and exposure, with calibration achieved through the use of acrylic mirrors and geometric alignment grids.

**Anatomical Models.** We utilized a half-body vascular phantom model from Elastrat Sarl Ltd., Switzerland, enclosed in a transparent box and integrated into a closed water circuit to simulate blood flow. Made from soft silicone and equipped with compact continuous flow pumps, it replicates human blood flow dynamics. The design is based on detailed postmortem vascular casts, ensuring anatomical accuracy reflective of human vasculature, facilitating realistic vascular simulations.

**Figure 1**. Dataset Overview: Guide3D contains 8,746 manually annotated frames from two views for 3D reconstruction (left), from which the reconstruction is derived (right).**Surgical Tools.** To enhance our dataset, we navigated complex vascular structures using two types of guidewires commonly used in real-world endovascular surgery. The first, the Radifocus™ Guide Wire M Stiff Type (Terumo Ltd.), is made from nitinol with a polyurethane-tungsten coating. It measures 0.89 mm in diameter and 260 cm in length, with a 3 cm angled tip, designed for seeking, dissecting, and crossing lesions. The second, the Nitrex Guidewire (Nitrex Metal Inc.), also made of nitinol, features a gold-tungsten straight tip for enhanced radiopacity in fluoroscopic visualization. It has a diameter of 0.89 mm and a length of 400 cm, with a 15 cm tip, and is generally used for accessing or maintaining position during catheter exchanges. Both guidewires were selected to reflect real-world usage and to diversify the data in our dataset.

**Figure 2**. Materials: a) Overall setup & endovascular phantom, b) Radifocus (angled) guidewire. and c) Nitrex (straight) guidewire.## Data Acquisition, Labeling, and Statistics

Using the materials described in Subsection 3.1, we compiled a dataset of 8,746 high-resolution samples (1,024 × 1,024 pixels). This dataset includes 4,373 paired instances, both with and without a simulated blood flow medium. Specifically, it consists of 6,136 samples from the Radifocus guidewire and 2,610 from the Nitrex guidewire, providing a solid foundation for automated guidewire tracking in bi-planar scanner images. Manual annotation was carried out using the Computer Vision Annotation Tool (CVAT), where polylines were created to accurately track the dynamic path of the guidewires. The polyline representation was chosen because the guidewire's structure often results in overlapping sections, making a segmentation mask unsuitable. In contrast, a polyline effectively captures the looping nature of the guidewire, offering greater accuracy.

As shown in Table 1, the dataset includes 3,664 instances of angled guidewires with fluid and 484 without, while straight guidewires are represented by 2,472 instances with fluid and 2,126 without. This distribution reflects a variety of procedural contexts. All 8,746 images in the dataset are accompanied by manual segmentation ground truth, facilitating the development of algorithms that require segmentation maps as reference data.

**Table 1**. Dataset Composition Overview.## Calibration

We extract the camera parameters using a traditional undistortion and calibration method. Undistortion is first achieved with a local weighted mean (LWM) algorithm, using a perforated steel sheet with a hexagonal pattern as a framing reference, and applying a blob detection algorithm to precisely identify distortion points. This approach establishes correspondences between distorted and undistorted positions, allowing for accurate distortion correction.

Following this, a semi-automatic calibration step is performed for marker identification, and the random sampling consensus (RANSAC) method is used to ensure robustness in computing the projection matrix and deriving the intrinsic and extrinsic camera parameters. The calibration process is further refined through direct linear transformation (DLT) and non-linear optimization, utilizing multiple poses of the calibration object to optimize the overall camera setup. Figure 3 illustrates the calibration process.

**Figure 3**. Fluoroscopic Calibration: a) Undistortion grid application, and b) Point identification on calibration frame.## Guidewire Reconstruction

Given polyline representations of a curve in both planes, the reconstruction process begins by parameterizing these curves using B-Spline interpolation. Each curve is expressed as a function of the cumulative distance along its path. Let $\mathbf{C}_A(\mathbf{u}_A)$ and $\mathbf{C}_B(\mathbf{u}_B)$ represent the parameterized B-Spline curves in their respective planes, where $\mathbf{u}_A$ and $\mathbf{u}_B$ are the normalized arc-length parameters. The corresponding $\mathbf{u}_B$ for a given $\mathbf{u}_A$ is found using epipolar geometry. Once the corresponding points $\mathbf{C}_A(\mathbf{u}_A^i)$ and $\mathbf{C}_B(\mathbf{u}_B^i)$ are identified, their 3D coordinates $\mathbf{P}^i$ are computed by triangulation, resulting in a set of 3D points $\{\mathbf{P}^i\}_{i=1}^{M}$, where $M$ is the total number of sampled points. This effectively reconstructs the original curve in 3D space.

To retrieve the fundamental matrix $\mathbf{F}$, which describes the relationship between points in Image A ($\mathbf{I}_A$) and Image B ($\mathbf{I}_B$), the condition $\mathbf{x}_B^T \mathbf{F} \mathbf{x}_A = 0$ must hold for corresponding points $\mathbf{x}_A$ in $\mathbf{I}_A$ and $\mathbf{x}_B$ in $\mathbf{I}_B$. Using the projection matrices $\mathbf{P}_A$ and $\mathbf{P}_B$ derived from the calibration process, the fundamental matrix can be calculated as follows:

Here, $\mathbf{e}_B$ is the epipole in Image B, defined as $\mathbf{e}_B = \mathbf{P}_B \mathbf{C}_A$, with $\mathbf{C}_A$ being the camera center of $\mathbf{P}_A$. The skew-symmetric matrix of the epipole $\mathbf{e}_B$ is represented by:

Where $\mathbf{e}_B = (e_{B1}, e_{B2}, e_{B3})^T$, and $\mathbf{P}_A^+$ is the pseudoinverse of the projection matrix $\mathbf{P}_A$. The fundamental matrix $\mathbf{F}$ encapsulates the epipolar geometry between the two views, ensuring that corresponding points $\mathbf{x}_A$ and $\mathbf{x}_B$ lie on their respective epipolar lines.

The matching phase begins by uniformly sampling points along the curve $\mathbf{C}_A(u_A)$ at intervals $\Delta u_A$. For each sampled point $x_A = \mathbf{C}_A(u_A)$, we project the epiline $l_B = F x_A$ into Image B. We then determine the intersection of the epiline $l_B$ with the curve $\mathbf{C}_B(u_B)$, thereby obtaining the corresponding parameter $u_B$ for each $u_A$.

Due to errors in the projection matrices $P_A$ and $P_B$, there are instances where the epiline $l_B$ does not intersect with any part of the curve $\mathbf{C}_B$. To address this, we fit a monotonic function $f_A(u_A) \rightarrow u_B$ using a Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), thus interpolating the missing intersections. The matching process is visualized in Fig. 4.

**Figure 4**.Point Matching Process. Sampled points from image $I_A$ ($\mathbf{C}_A(u_A)$) and their corresponding epilines $l_A$ on image $I_B$ are matched with their counterparts $\mathbf{C}_B(u_B)$. The epilines for $\mathbf{C}_B(u_B)$ are then computed and displayed on image $I_A$.## Utility of Guide3D Dataset for the Research Community

Guide3D advances endovascular imaging by providing a bi-planar fluoroscopic dataset for segmentation and 3D reconstruction, serving as an open-source benchmark. It enables precise algorithm comparisons for segmentation and facilitates method development in 3D reconstruction through the use of bi-planar imagery. With video data, Guide3D supports video-based methods, leveraging temporal dimensions for dynamic analysis. This enriches the segmentation and reconstruction capabilities, while also aligning with the procedural nature of endovascular interventions. This versatility highlights Guide3D's pivotal role in advancing endovascular imaging.

## Next

In the next part, we will explore Guidewire Shape Prediction methodology.