A new 2D‐3D registration gold‐standard dataset for the hip joint based on uncertainty modeling

D'Isidoro, Fabio; Chênes, Christophe; Ferguson, Stephen J.; Schmid, Jérôme

doi:10.1002/mp.15124

Cited by 4 publications

(5 citation statements)

References 71 publications

(255 reference statements)

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“…To overcome the impact of the above methods that cannot assess the variable contrast and mobility of soft tissues, Pawiro et al [109] developed a pig head dataset with additional benchmark markers to obtain realistic data with bones and various soft tissues for accurate validation of 3D-2D registration algorithms. In addition, D'Isidoro et al [110] proposed a standardized evaluation method for 3D-2D registration of the hip that introduces a gold standard validation dataset consisting of a CT scan of a female hip and 19 2D fluoroscopic images in different views, while using a multiple projection point criterion to reduce the effect of noise. To enhance the generalizability of gold datasets, Madan et al [111] proposed a framework for automatic generation of gold standard registration datasets.…”

Section: Evaluation Of 3d-2d Registration Methodsmentioning

confidence: 99%

A Review of 3D-2D Registration Methods and Applications based on Medical Images

Sun¹

2023

HSET

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The registration of preoperative three-dimensional (3D) medical images with intraoperative two-dimensional (2D) data is a key technology for image-guided radiotherapy, minimally invasive surgery, and interventional procedures. In this paper, we review 3D-2D registration methods using computed tomography (CT) and magnetic resonance imaging (MRI) as preoperative 3D images and ultrasound, X-ray, and visible light images as intraoperative 2D images. The 3D-2D registration techniques are classified into intensity-based, structure-based, and gradient-based according to the different registration features. In addition, we investigated the different application scenarios of this registration technology in medical clinical treatment, which can be divided into disease diagnosis, surgical guidance and postoperative evaluation, and also investigated the evaluation method of 3D-2D registration effect.

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Section: Evaluation Of 3d-2d Registration Methodsmentioning

confidence: 99%

A Review of 3D-2D Registration Methods and Applications based on Medical Images

Sun¹

2023

HSET

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“…In this section, we show the performance of experiments in a public clinical dataset, Hip Joint gold-standard Dataset [5]. This solid work collects a full scan computed tomography of a female patient.…”

Section: Methodsmentioning

confidence: 99%

“…The 2D-3D registration is widely applied in Autonomous Vehicles [1], [2], Image-Guided Intervention [3], and Robotics [4]. In Image-guided Intervention of Hip Joint [5], pre-operative 3D CT is implemented to provide the patient's information to plan the surgery [6], [7]. While in the intraoperative stage, the 2D X-rays from different viewpoints are used to observe the updated state of the patient, for the navigation of the surgical instrument.…”

Section: Introductionmentioning

confidence: 99%

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Globally Learnable Point Set Registration Between 3D CT and Multi-view 2D X-ray Images of Hip Phantom

Pan

Min

Zhang

et al. 2021

2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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2D-3D registration is a crucial step in Image-Guided Intervention, such as spine surgery, total hip replacement, and kinematic analysis. To find the information in common between pre-operative 3D CT images and intraoperative X-ray 2D images is vital to plan and navigate. In a nutshell, the goal is to find the movement and rotation of the 3D body's volume to make them reorient with the patient body in the 2D image space. Due to the loss of dimensionality and different sources of images, efficient and fast registration is challenging. To this end, we propose a novel approach to incorporate a point set Neural Network to combine the information from different views, which enjoys the robustness of the traditional method and the geometrical information extraction ability. The pre-trained Deep BlindPnP captures the global information and local connectivity, and each implementation of view-independent Deep BlindPnP in different view pairs will select top-priority pairs candidates. The transformation of different viewpoints into the same coordinate will accumulate the correspondence. Finally, a POSEST-based module will output the final 6 DoF pose. Extensive experiments on a real-world clinical dataset show the effectiveness of the proposed framework compared to the single view. The accuracy and computation speed are improved by incorporating the point set neural network.

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“…Three-dimensional pose estimation is one of the most active topics in computer vision research. Effective algorithms that use 2D–3D point correspondences between pairs of images have been developed in several ways [ 1 , 2 ]. However, these techniques cannot be directly applied to transmission images (i.e., fluoroscopic images) because of complications caused by inconvenient calibration objects or the failure of feature matching algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comparison of 3D Bone Position Estimation Using QR Code and Metal Bead Markers

Ikumi

Yoshii

Iwahashi³

et al. 2023

Diagnostics

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To improve the accuracy of a 3D bone position estimation system that displays 3D images in response to changes in the position of fluoroscopic images, modified markers using quick response (QR) codes were developed. The aims of this study were to assess the accuracy of the estimated bone position on 3D images with reference to QR code markers on fluoroscopic images and to compare its accuracy with metal bead markers. Bone positions were estimated from reference points on a fluoroscopic image compared with those on a 3D image. The positional relationships of QR code and metal bead markers on the fluoroscopic image were compared with those on the 3D image in order to establish whether a 3D image may be drawn by tracking positional changes in radius models. Differences were investigated by comparing the distance between markers on the fluoroscopic image and that on the 3D image, which was projected on the monitor. The error ratio, which was defined as the difference in the measurement between the fluoroscopic and 3D images divided by the fluoroscopic measurement, was compared between QR code and metal bead markers. Error ratios for the QR code markers were 5.0 ± 2.0%, 6.4 ± 7.6%, and 1.0 ± 0.8% in the anterior–posterior view, ulnar side lateral view, and posterior–anterior view, respectively. Error ratios for the metal bead markers were 1.3 ± 1.7%, 13.8 ± 14.5%, and 4.7 ± 5.7% in the anterior–posterior view, ulnar side lateral view, and posterior–anterior view, respectively. The error ratio for the metal bead markers was smaller in the initial position (p < 0.01). However, the error ratios for the QR code markers were smaller in the lateral position and the posterior–anterior position (p < 0.05). In QR code marker tracking, tracking was successful even with discontinuous images. The accuracy of a 3D bone position estimation was increased by using the QR code marker system. QR code marker tracking facilitates real-time comparisons of dynamic changes in preoperative 3D and intraoperative fluoroscopic images.

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A new 2D‐3D registration gold‐standard dataset for the hip joint based on uncertainty modeling

Abstract: A new 2D-3D registration gold-standard dataset for the hip joint based on uncertainty modeling.

Cited by 4 publications

References 71 publications

A Review of 3D-2D Registration Methods and Applications based on Medical Images

A Review of 3D-2D Registration Methods and Applications based on Medical Images

Globally Learnable Point Set Registration Between 3D CT and Multi-view 2D X-ray Images of Hip Phantom

Comparison of 3D Bone Position Estimation Using QR Code and Metal Bead Markers

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