Guide3D: A Bi-planar X-ray Dataset for Guidewire Segmentation and 3D Reconstruction

Tudor Jianu, Baoru Huang, Hoan Nguyen, Binod Bhattarai, Tuong Do, Erman Tjiputra, Quang Tran, Pierre Berthet-Rayne, Ngan Le, Sebastiano Fichera, Anh Nguyen; Proceedings of the Asian Conference on Computer Vision (ACCV), 2024, pp. 1549-1565

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 code and dataset will be made publicly available to encourage further studies.