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

TL;DR

Guide3D introduces a high-resolution bi-planar X-ray dataset for improved 3D shape reconstruction in endovascular surgery, enabling better machine learning development and clinical application validation.

Contribution

We provide the first publicly available bi-planar X-ray dataset for 3D reconstruction and establish a new benchmark for guidewire shape prediction.

Findings

Dataset validated in clinical-like simulated environments.

Baseline benchmark established for guidewire shape prediction.

Facilitates advancement of segmentation and 3D reconstruction techniques.

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/.