Automatic Cone Beam Projection-based Liver Tumor Localization by Deep Learning and Biomechanical Modeling

Zhang, Y.; Huang, Xinda; Wang, J.

doi:10.1016/j.ijrobp.2020.07.946

Cited by 2 publications

(8 citation statements)

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“…In our review we found that unsupervised representation learning techniques are a widely adopted technique to reduce the dimensionality of the parameter space while introducing implicit regularization by confining possible solutions to the principal modes of variation across population- or patient-level observations. We identified 12 studies that propose such techniques or use them as part of the registration pipeline ( Brost et al, 2012 ; Lin and Winey, 2012 ; Chou and Pizer, 2013 , 2014 ; Chou et al, 2013 ; Zhao et al, 2014 ; Baka et al, 2015 ; Pei et al, 2017 ; Chen et al, 2018 ; Zhang et al, 2018 ; Foote et al, 2019 ; Li et al, 2020 ; Zhang et al, 2020 ).…”

Section: Systematic Reviewmentioning

confidence: 99%

“…PCA is by far the most prevalent method for representation learning and is used in all but one study. This specific study, however, used by far the most views v = 20 for initial estimation of a low resolution vector field, which was then regularized by projection onto a deep learning-based population model ( Zhang et al, 2020 ). We found that methods designed for cephalometry were distinct from all other approaches as their primary goal is not generally 2D/3D registration, but 3D reconstruction of the skull given a 2D X-ray.…”

Section: Systematic Reviewmentioning

confidence: 99%

“…2 Indeed, among the seven studies that trained deep CNNs on synthetic data and evaluated in a way that allowed for comparisons between synthetic and real data performance, we found quite substantial performance drops ( Miao et al, 2018 ; Bier et al, 2019 ; Gao et al, 2020c ; Doerr et al, 2020 ; Gu et al, 2020 ; Guan et al, 2020 ; Li et al, 2020 ). Worse, three studies used different evaluation metrics in synthetic and real experiments so that comparison was not possible ( Miao et al, 2016a ; Toth et al, 2018 ; Esfandiari et al, 2021 ), and perhaps worst, ten studies that trained on synthetic data never even tested (meaningfully) on real data ( Hou et al, 2017 , 2018 ; Pei et al, 2017 ; Xie et al, 2017 ; Bier et al, 2018 ; Foote et al, 2019 ; Guan et al, 2019 ; Yang and Chen, 2019 ; Neumann et al, 2020 ; Zhang et al, 2020 ). To mitigate the negative impact of domain shift, there is growing interest in domain adaptation and generalization techniques.…”

Section: Perspectivementioning

confidence: 99%

“…Quantitatively evaluating registration performance is clearly important. 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 ).…”

Section: Perspectivementioning

confidence: 99%

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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.

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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

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show abstract

Section: Representation Learningmentioning

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

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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

Automatic Cone Beam Projection-based Liver Tumor Localization by Deep Learning and Biomechanical Modeling

Cited by 2 publications

References 0 publications

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

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

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

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