Comparative Evaluation of Regression Methods for 3D-2D Image Registration

Gouveia, Ana Isabel; Metz, Coert; Freire, Luís; Klein, Stefan

doi:10.1007/978-3-642-33266-1_30

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…Machine learning-based improvements to image-based 2D/3D registration were already of interest before the deep learning era ( Gouveia et al, 2012 ), and deep learning has only accelerated and diversified the contributions to the field. Contextualization of data, representation learning to reduce problem dimensionality, similarity modeling for increased capture range, direct pose regression to avoid iterative optimization, as well as confidence assessment are all well established research thrusts, which are geared towards developing automated registration pipelines.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…For this regression problem with such images and input-output set, the MLP is an adequate choice with higher performance when compared with other suitable possibilities (Gouveia et al 2012b). …”

Section: Methods and Materials 21 Registration By Nonlinear Regressimentioning

confidence: 99%

“…All cases have one output unit, which is the correspondent transformation parameter. From optimisation studies in Gouveia et al (2012aGouveia et al ( , 2012b, the optimum number of hidden units for combination F1 was set to the double of the number of input units. Since the combinations F2, F3 and F4 had similar number of features, we adopted the same rule here.…”

Section: Regression Modelmentioning

confidence: 99%

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Registration-by-regression of coronary CTA and X-ray angiography

Gouveia

Metz

Freire

et al. 2015

Computer Methods in Biomechanics and Biomedical Engineering: Im

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We evaluate the integration of 3D preoperative computed tomography angiography of the coronary arteries with intraoperative 2D X-ray angiographies by a recently proposed novel registration-by-regression method. The method relates image features of 2D projection images to the transformation parameters of the 3D image. We compared different sets of features and studied the influence of preprocessing the training set. For the registration evaluation, a gold standard was developed from eight X-ray angiography sequences from six different patients. The alignment quality was measured using the 3D mean target registration error (mTRE). The registration-by-regression method achieved moderate accuracy (median mTRE of 15 mm) on real images. It does therefore not provide yet a complete solution to the 3D-2D registration problem but it could be used as an initialisation method to eliminate the need for manual initialisation.

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“…Gouveia et al compared multiple regression approaches for rigid 2D-to-3D registration approaches applied to simulated X-ray registration problems. 2 Miao et al used convolutional neural networks to learn a regression of rigid registration parameters in 2D-to-3D registration. 3 Gutiérrez-Becker et al developed a method that uses regression forests to learn multimodal motion predictors.…”

Section: Related Work On Machine Learning In Medical Image Registrationmentioning

confidence: 99%

Deformable image registration using convolutional neural networks

Eppenhof

Lafarge

Moeskops

et al. 2018

Medical Imaging 2018: Image Processing

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Comparative Evaluation of Regression Methods for 3D-2D Image Registration

Cited by 5 publications

References 11 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

Registration-by-regression of coronary CTA and X-ray angiography

Deformable image registration using convolutional neural networks

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