Registration of organs with sliding interfaces and changing topologies

Berendsen, Floris G; Kotte, Antj Alexis; Viergever, MA Max; Pluim, Jpw Josien

doi:10.1117/12.2043447

Cited by 17 publications

(30 citation statements)

References 11 publications

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“…In our experiments, we employed XFFD as the transformation model and adopted normalized crosscorrelation as similarity metric, and Limited memory Broyden-Fletcher-Goldfarb- Shanno (LBFGS) as the optimizer, because cross-correlation is robust to linear variations in image intensity (Penney et al, 1998) and LBFGS is known for its high performance in dealing with high-dimensional problems (Zhu et al, 1997). For the sake of fair comparison, we selected similar parameters to the previous work in (Berendsen et al, 2014). …”

Section: Methodsmentioning

confidence: 99%

“…The DIR-lab dataset was adopted in most of the previous methods treating discontinuities. We computed the same measurements, TRE, gap and overlap volumes, as reported in all the B-spline based methods (Wu et al, 2008;Delmon et al, 2013;Berendsen et al, 2014) and some diffusion-based methods (Schmidt-Richberg et al, 2012;Pace et al, 2013;Papież et al, 2014).…”

Section: Comparison With Previous Methodsmentioning

confidence: 99%

“…In their experiments, they used a motion mask covering also the abdomen, based on the assumption that the lungs and the abdomen move continuously together, although this assumption is not accurate, as the lungs and the abdominal organs slide relative to each other (Pace et al, 2011). This restriction in the shape of the discontinuity interface was alleviated by using multiple B-spline transformations covering the regions separated by the discontinuity interface, and a penalty term to reduce gaps and overlaps at the region boundaries (Berendsen et al, 2014).…”

Section: Previous Workmentioning

confidence: 99%

“…To evaluate this effect, the surface mesh describing the discontinuity interface was transformed by considering it to belong to either the inside or the outside, producing two transformed meshes, (Wu et al, 2008;Delmon et al, 2013;Berendsen et al, 2014).…”

Section: Metrics For Evaluationmentioning

confidence: 99%

“…We computed the Euclidean distance between the landmarks in the moving image and those in the target image, displaced by the deformation field. The mean and standard deviation of the distance of all the landmarks in each case was reported as the TRE Target Registration Error (TRE) (Schmidt-Richberg et al, 2012;Pace et al, 2013;Papież et al, 2014;Wu et al, 2008;Delmon et al, 2013;Berendsen et al, 2014).…”

Section: Metrics For Evaluationmentioning

confidence: 99%

See 4 more Smart Citations

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

Hua

Pozo

Taylor

et al. 2017

Medical Image Analysis

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Image registration is an essential technique to obtain point correspondences between anatomical structures from different images. Conventional nonrigid registration methods assume a continuous and smooth deformation field throughout the image. However, the deformation field at the interface of different organs is not necessarily continuous, since the organs may slide over or separate from each other. Therefore, imposing continuity and smoothness ubiquitously would lead to artifacts and increased errors near the discontinuity interface.In computational mechanics, the eXtended Finite Element Method (XFEM) was introduced to handle discontinuities without using computational meshes that conform to the discontinuity geometry. Instead, the interpolation bases themselves were enriched with discontinuous functional terms. Borrowing this concept, we propose a multiresolution eXtented Free-Form Deformation (XFFD) framework that seamlessly integrates within and extends the standard Free-Form Deformation (FFD) approach. Discontinuities are incor- Preprint submitted to ElsevierOctober 18, 2016 porated by enriching the B-spline basis functions coupled with extra degrees of freedom, which are only introduced near the discontinuity interface. In contrast with most previous methods, restricted to sliding motion, no ad hoc penalties or constraints are introduced to reduce gaps and overlaps. This allows XFFD to describe more general discontinuous motions. In addition,we integrate XFFD into a rigorously formulated multiresolution framework by introducing an exact parameter upsampling method.The proposed method has been evaluated in two publicly available datasets:4D pulmonary CT images from the DIR-Lab dataset and 4D CT liver datasets.The XFFD achieved a Target Registration Error (TRE) of 1.17 ± 0.85 mm in the DIR-lab dataset and 1.94 ± 1.01 mm in the liver dataset, which significantly improves on the performance of the state-of-the-art methods handling discontinuities.

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Section: Methodsmentioning

confidence: 99%

Section: Comparison With Previous Methodsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Metrics For Evaluationmentioning

confidence: 99%

Section: Metrics For Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

Hua

Pozo

Taylor

et al. 2017

Medical Image Analysis

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show abstract

TopAwaRe: Topology-Aware Registration

Nielsen

Darkner

Feragen

2019

Lecture Notes in Computer Science

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An Unsupervised Learning Approach to Discontinuity-Preserving Image Registration

Ebrahimi

2020

Lecture Notes in Computer Science

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Most traditional image registration algorithms aimed at aligning a pair of images impose well-established regularizers to guarantee smoothness of unknown deformation fields. Since these methods assume global smoothness within the image domain, they pose issues for scenarios where local discontinuities are expected, such as the sliding motion between the lungs and the chest wall during the respiratory cycle. Furthermore, an objective function must be optimized for each given pair of images, thus registering multiple sets of images become very time-consuming and scale poorly to higher resolution image volumes.Using recent advances in deep learning, we propose an unsupervised learning-based image registration model. The model is trained over a loss function with a custom regularizer that preserves local discontinuities, while simultaneously respecting the smoothness assumption in homogeneous regions of image volumes. Qualitative and quantitative validations on 3D pairs of lung CT datasets will be presented.

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Registration of organs with sliding interfaces and changing topologies

Cited by 17 publications

References 11 publications

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

TopAwaRe: Topology-Aware Registration

An Unsupervised Learning Approach to Discontinuity-Preserving Image Registration

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