Estimation of the deformations induced by articulated bodies: Registration of the spinal column

d'Aische, Aloys du Bois; Craene, Mathieu De; Geets, Xavier; Vincent, Grégoire; Macq, Benoı̂t; Warfield, Simon K.

doi:10.1016/j.bspc.2007.03.002

Cited by 17 publications

(21 citation statements)

References 13 publications

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“…One can imagine many different solutions to overcome this issue, such as a potentially more realistic deformation model (e.g. finite element methods [5][1]) or the incorporation of soft tissue image intensities into the registration process with rigidity constraints for the bones [15]. However, the approach we pursue is including further landmarks -the body surface -as additional constraints.…”

Section: Methodsmentioning

confidence: 99%

“…Rarely, the evaluation of the registration accuracy is carried out quantitatively. In [1], the center of volume error and dice similarities are reported for manually delineated targets whereas the distance of 20 landmarks is reported in [5]. Both evaluations are based on only 4 pairs of images.…”

Section: Introductionmentioning

confidence: 99%

“…The deformation of the surrounding soft tissue can be computed solely based on the rigid transformations using thin plate splines (TPS) [7], linear combinations of transformations [9] [14][10], a LogEuclidean framework [3] or finite element methods (FEM) [5] [1]. Alternatively, the rigid parts can be incorporated as constraints [15] or serve as an initialization [13] [8] for an overall deformable intensity based registration.…”

Section: Introductionmentioning

confidence: 99%

“…Originally proposed by Little et al [9], many other works are based on this principle. It is mostly applied to the registration of the spine [7][6][15] [14], but also to the head&neck [5][1], the brain [3], the hand [10] and even to the whole body [8]. On Micro-CTs it is used for the registration of the lower limbs [11] and the whole body [4] [7] pursue semi-automatic approaches while [10][14] present very specific automatic heuristics.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Skeleton Based Multi-modal Deformable Registration of Head&Neck Datasets

Steger

Wesarg

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. This paper presents a novel skeleton based method for the registration of head&neck datasets. Unlike existing approaches it is fully automated, spatial relation of the bones is considered during their registration and only one of the images must be a CT scan. An articulated atlas is used to jointly obtain a segmentation of the skull, the mandible and the vertebrae C1-Th2 from the CT image. These bones are then successively rigidly registered with the moving image, beginning at the skull, resulting in a rigid transformation for each of the bones. Linear combinations of those transformations describe the deformation in the soft tissue. The weights for the transformations are given by the solution of the Laplace equation. Optionally, the skin surface can be incorporated. The approach is evaluated on 20 CT/MRI pairs of head&neck datasets acquired in clinical routine. Visual inspection shows that the segmentation of the bones was successful in all cases and their successive alignment was successful in 19 cases. Based on manual segmentations of lymph nodes in both modalities, the registration accuracy in the soft tissue was assessed. The mean target registration error of the lymph node centroids was 5.33 ± 2.44 mm when the registration was solely based on the deformation of the skeleton and 5.00 ± 2.38 mm when the skin surface was additionally considered. The method's capture range is sufficient to cope with strongly deformed images and it can be modified to support other parts of the body. The overall registration process typically takes less than 2 minutes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Skeleton Based Multi-modal Deformable Registration of Head&Neck Datasets

Steger

Wesarg

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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“…They register the bone structures and then this transformation is refined for the other tissues. du Bois d'Aische et al [9], present an articulated registration method for the spine column, based on the finite elements method for the elastic deformation outside rigid structures. No invertibility study for these methods, nor an evaluation of the sensitivity of the results with respect to the weighting functions used to fuse the set of rigid transformations are presented.…”

Section: Introductionmentioning

confidence: 99%

A Log-Euclidean Polyaffine Registration for Articulated Structures in Medical Images

Martín-Fernández

Martı́n-Fernández

Alberola-López

2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. In this paper we generalize the Log-Euclidean polyaffine registration framework of Arsigny et al.[1] to deal with articulated structures. This framework has very useful properties as it guarantees the invertibility of smooth geometric transformations. In articulated registration a skeleton model is defined for rigid structures such as bones. The final transformation is affine for the bones and elastic for other tissues in the image. We extend the Arsigny el al.'s method to deal with locally-affine registration of pairs of wires. This enables the possibility of using this registration framework to deal with articulated structures. In this context, the design of the weighting functions, which merge the affine transformations defined for each pair of wires, has a great impact not only on the final result of the registration algorithm, but also on the invertibility of the global elastic transformation. Several experiments, using both synthetic images and hand radiographs, are also presented.

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Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods

Goedmakers

Pereboom²,

Schoones³

et al. 2022

Brain and Spine

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Estimation of the deformations induced by articulated bodies: Registration of the spinal column

Cited by 17 publications

References 13 publications

Automated Skeleton Based Multi-modal Deformable Registration of Head&Neck Datasets

Automated Skeleton Based Multi-modal Deformable Registration of Head&Neck Datasets

A Log-Euclidean Polyaffine Registration for Articulated Structures in Medical Images

Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods

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