Guide3D: A Bi-planar X-ray Dataset for 3D Shape Reconstruction

Abstract

Endovascular surgical tool reconstruction represents an important factor in advancing endovascular tool navigation, which is an important step in endovascular surgery. However, the lack of publicly available datasets significantly restricts the development and validation of novel machine learning approaches. Moreover, due to the need for specialized equipment such as biplanar scanners, most of the previous research employs monoplanar fluoroscopic technologies, hence only capturing the data from a single view and significantly limiting the reconstruction accuracy. To bridge this gap, we introduce Guide3D, a bi-planar X-ray dataset for 3D reconstruction. The dataset represents a collection of high resolution bi-planar, manually annotated fluoroscopic videos, captured in real-world settings. Validating our dataset within a simulated environment reflective of clinical settings confirms its applicability for real-world applications. Furthermore, we propose a new benchmark for guidewrite shape prediction, serving as a strong baseline for future work. Guide3D not only addresses an essential need by offering a platform for advancing segmentation and 3D reconstruction techniques but also aids the development of more accurate and efficient endovascular surgery interventions. Our project is available at https://airvlab.github.io/guide3d/.

Figures (7)

• 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).
• Figure 2: Materials:a) Overall setup & endovascular phantom, b) Radifocus (angled) guidewire. and c) Nitrex (straight) guidewire.
• Figure 3: Fluoroscopic Calibration:a) Undistortion grid application, and b) Point identification on calibration frame.
• Figure 4: Network Key Components: The figure illustrates essential components of the proposed model. a) Spherical coordinates $(r, \theta, \phi)$ used for predicting the guidewire shape. b) The model predicts the 3D shape of a guidewire from image sequences $\mathbf{I}_t$. A Vision Transformer (ViT) extracts spatial features $\mathbf{z}_t$, which a Gated Recurrent Unit (GRU) processes to capture temporal dependencies, producing hidden states $\mathbf{h}_t$. The final hidden state drives three prediction heads: the Tip Prediction Head for 3D tip position $\mathbf{p} \in \mathbb{R}^3$, the Spherical Offset Prediction Head for coordinate offsets $(\Delta\phi, \Delta\theta)$, and the Stop Prediction Head for terminal point probability $\mathbf{S}$.
• Figure 5: Guidewire Reconstruction Error Analysis: (Left) Illustrates the distribution of reprojection errors, noting higher variability and peak errors in the mid-sections and reduced errors at the extremities. (Right) Presents the results of reconstruction validation.