AIOZ AI / Oct 15th / 7 min read

Guide3D A Bi-planar X-ray Dataset for 3D Shape Reconstruction (Part 1)

Introduction about 3D Shape Reconstruction

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, 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. The proposal 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.

Introduction

Minimally invasive surgery has revolutionized endovascular interventions, offering less invasive options with shorter recovery times. The success of these procedures depends on the precise navigation and manipulation of instruments such as guidewires and catheters. Typically, 2D visualization methods are used for guidance, with monoplanar fluoroscopy being the most common due to its minimal disruption to surgical workflows and relatively affordable cost. However, despite their widespread use, conventional imaging techniques have significant limitations, with one of the primary challenges being the lack of depth perception. This issue complicates the accurate visualization of surgical instruments, increasing the risk of excessive contact with arterial walls, which can compromise patient safety and the effectiveness of the procedure.

In endovascular interventions, depth perception is largely achieved through multi-view imaging systems, such as biplanar scanners, which allow shape reconstruction by combining images from multiple angles and employing epipolar geometry-based reconstruction. However, two major challenges hinder the broader adoption and effectiveness of these systems: (i) the difficulty of accurately segmenting images for successful shape reconstruction, exacerbated by the scarcity of datasets needed to evaluate segmentation methods, and (ii) the limited availability of specialized biplanar scanners in clinical settings due to their high cost. These challenges underscore the critical need for comprehensive datasets to enhance segmentation algorithm accuracy and improve guidewire reconstruction techniques, facilitating the development of more versatile imaging technologies.

Figure 1. Guide3D dataset contains 8,746 manually annotated frames from two views for 3D reconstruction.

In this paper, we introduce Guid3D, a dataset designed to advance 3D reconstruction in endovascular navigation. Guid3D provides a standardized platform for the development and evaluation of algorithms. With a comprehensive dataset that includes manual annotations for segmentation and tools for effective 3D visualization, Guid3D is intended to drive innovation and improvement in endovascular intervention. Furthermore, the inclusion of video-based biplanar fluoroscopic data allows for the exploration of temporal dynamics, such as using optical flow networks. Guid3D seeks to bridge the gap between research innovations and clinical applications, addressing key challenges in endovascular procedures.

Related Works

Endovascular Datasets.

Datasets play a crucial role in advancing endovascular navigation by providing essential resources for the development, evaluation, and enhancement of algorithms. These datasets, derived from various imaging modalities such as mono X-ray, 3D ultrasound, and 3D MRI, encompass both real and synthetic images, facilitating diverse applications in the medical field.

Mono X-ray datasets, while prevalent, often fall short in providing the necessary detail required for accurate 3D reconstruction, which is critical for effective surgical navigation. The inherent limitations of 2D imaging techniques make it challenging to fully capture the complexity of anatomical structures during procedures. In contrast, 3D imaging modalities like 3D ultrasound and 3D MRI offer more comprehensive views, enabling better depth perception and improved visualization of surgical tools and surrounding tissues.

Despite the importance of these datasets, there remains a significant gap in the availability of comprehensive, publicly accessible datasets specifically designed for tool segmentation and 3D reconstruction. This scarcity hampers progress in developing robust algorithms capable of accurately interpreting complex medical images. The lack of diverse and high-quality datasets also limits the ability of researchers to train and validate their algorithms effectively, often leading to suboptimal performance in clinical scenarios.

Furthermore, creating high-quality datasets is not merely a technical challenge; it requires collaboration among various stakeholders, including clinicians, radiologists, and data scientists. Such collaboration is essential to ensure that the datasets reflect real-world clinical conditions and include diverse patient populations. Expanding the availability of well-annotated datasets is vital for fostering innovation and advancing the field of endovascular surgery.

Figure 2. Endovascular Dataset Explaination.

Catheter and Guidewire Segmentation.

The segmentation of endovascular tools, particularly guidewires and catheters, is an evolving field that heavily relies on the availability and quality of datasets. Previous studies have often used synthetic and semi-synthetic data to address the challenges posed by the limited availability of real-world datasets. Researchers have employed manually annotated datasets from 2D X-ray and 3D MRI modalities to train segmentation models. Additionally, the effectiveness of synthetic datasets has been demonstrated in improving model efficiency.

The advent of deep learning techniques, especially U-Net architectures, has significantly enhanced the accuracy of segmentation and tracking for these surgical instruments. This advancement has led to the development of fully automated segmentation frameworks that utilize extensively annotated data and incorporate unsupervised techniques, such as optical flow. However, the absence of a public, standardized dataset for method comparison continues to impede the advancement and assessment of scientific progress in this area.

Figure 3. Interventional Microcatheters.

3D Reconstruction.

Improving the accuracy of 3D reconstruction in endovascular procedures plays a crucial role in achieving better clinical outcomes by enhancing catheter navigation through advanced visualization and precise tracking. Advances in fluoroscopic imaging technology have led to more accurate positioning of devices. Various algorithms have been developed to facilitate this process, employing techniques such as elastic grid registration and epipolar geometry for 3D reconstruction from biplane angiography. Additionally, automatic catheter detection methods utilizing triangulation and graph-search algorithms have been applied in electrophysiology studies to improve reconstruction outcomes.

Research has demonstrated the importance of accurate 3D models for navigation within both complex and single-view vascular architectures, highlighting the value of biplanar data. However, the limited availability of comprehensive, publicly accessible datasets for the development and validation of algorithms in 3D reconstruction poses a significant challenge to technological progress and clinical application. This situation underscores the critical need for specialized datasets to promote ongoing innovation in the reconstruction of endovascular tools.