Automatic Detection of Misalignment in Rigid 3D-2D Registration

Mitrović, Uroš; Špiclin, Žiga; Likar, Boštjan; Pernuš, Franjo

doi:10.1007/978-3-319-05666-1_15

Cited by 3 publications

(8 citation statements)

References 15 publications

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“…We identified four studies that leverage machine learning techniques for verifying whether a registration process produced satisfactory geometric parameter estimates ( Wu et al, 2016 ; Mitrovi et al, 2014 ; Varnavas et al, 2013 , 2015b ).…”

Section: Systematic Reviewmentioning

confidence: 99%

“…Similarly, Wu et al (2016) train a shallow NN to classify registration success based on hand-crafted features of the objective function surface around the registration estimate. Finally, Mitrovi et al (2014) compare a registration estimate to known local minima and thresholds to determine success/failure, which worked well but may be limited in practice as the approach seems to assume knowledge of the correct solution.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…The error metrics that were used included the standard registration pose error (commonly separated into translational and rotational DoFs), keypoint distances ( Chou and Pizer, 2014 ; Zhang et al, 2020 ), and the mean target registration error (mTRE) in 3D, and varied other metrics in 2D, such as reprojection distances ( Bier et al, 2019 ), segmentation or overlap DICE score ( Zhang et al, 2020 ), or contour differences ( Chen et al, 2018 ). Other metrics that are not uniquely attributable to a domain include the registration capture range ( Schaffert et al, 2019 ; Esfandiari et al, 2021 ) and the registration success rate ( Varnavas et al, 2013 ; Mitrovi et al, 2014 ; Miao et al, 2016b ; Yang and Chen, 2019 ; Schaffert et al, 2020a ). There are no standard routines to define the successful registrations.…”

Section: Perspectivementioning

confidence: 99%

“…The four studies which consider registration verification in this review all attempt to provide a low dimensional classification of registration success, e.g., pass/fail or correct/poor/incorrect ( Mitrovi et al, 2014 ; Varnavas et al, 2013 , 2015a ; Wu et al, 2016 ). These strategies effectively attempt to label registration estimates as either a global (correct) or local minimum (poor/incorrect) of (Eq.…”

Section: Perspectivementioning

confidence: 99%

See 3 more Smart Citations

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

Unberath

Gao

et al. 2021

Front. Robot. AI

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Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows. Furthermore, it is expected that image-based navigation techniques will play a major role in enabling mixed reality environments, as well as autonomous and robot-assisted workflows. A critical component of image guidance is 2D/3D registration, a technique to estimate the spatial relationships between 3D structures, e.g., preoperative volumetric imagery or models of surgical instruments, and 2D images thereof, such as intraoperative X-ray fluoroscopy or endoscopy. While image-based 2D/3D registration is a mature technique, its transition from the bench to the bedside has been restrained by well-known challenges, including brittleness with respect to optimization objective, hyperparameter selection, and initialization, difficulties in dealing with inconsistencies or multiple objects, and limited single-view performance. One reason these challenges persist today is that analytical solutions are likely inadequate considering the complexity, variability, and high-dimensionality of generic 2D/3D registration problems. The recent advent of machine learning-based approaches to imaging problems that, rather than specifying the desired functional mapping, approximate it using highly expressive parametric models holds promise for solving some of the notorious challenges in 2D/3D registration. In this manuscript, we review the impact of machine learning on 2D/3D registration to systematically summarize the recent advances made by introduction of this novel technology. Grounded in these insights, we then offer our perspective on the most pressing needs, significant open problems, and possible next steps.

show abstract

Section: Systematic Reviewmentioning

confidence: 99%

Section: Systematic Reviewmentioning

confidence: 99%

Section: Perspectivementioning

confidence: 99%

Section: Perspectivementioning

confidence: 99%

See 2 more Smart Citations

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective

Unberath

Gao

et al. 2021

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…We identified four studies that leverage machine learning techniques for verifying whether a registration process produced satisfactory geometric parameter estimates [79,80,36,37].…”

Section: Verificationmentioning

confidence: 99%

The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective

Unberath¹,

Cong²,

Yi-cheng³

et al. 2021

Preprint

View full text Add to dashboard Cite

Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows. Furthermore, it is expected that image-based navigation techniques will play a major role in enabling mixed reality environments, as well as autonomous and robot-assisted workflows. A critical component of image guidance is 2D/3D registration, a technique to estimate the spatial relationships between 3D structures, e. g., preoperative volumetric imagery or models of surgical instruments, and 2D images thereof, such as intraoperative X-ray fluoroscopy or endoscopy. While image-based 2D/3D registration is a mature technique, its transition from the bench to the bedside has been restrained by well-known challenges, including brittleness with respect to optimization objective, hyperparameter selection, and initialization, difficulties in dealing with inconsistencies or multiple objects, and limited single-view performance. One reason these challenges persist today is that analytical solutions are likely inadequate considering the complexity, variability, and high-dimensionality of generic 2D/3D registration problems. The recent advent of machine learning-based approaches to imaging problems that, rather than specifying the desired functional mapping, approximate it using highly expressive parametric models holds promise for solving some of the notorious challenges in 2D/3D registration. In this manuscript, we review the impact of machine learning on 2D/3D registration to systematically summarize the recent advances made by introduction of this novel technology. Grounded in these insights, we then offer our perspective on the most pressing needs, significant open problems, and possible next steps.

show abstract