Diffeomorphic surface-based registration for MR-US fusion in prostate brachytherapy

Cosse, Augustin

doi:10.1109/melcon.2012.6196575

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Such methods rely on lowdimensional transformations, such as rigid or affine, for registration. For example, Cosse et al [11] proposed an ICP-based algorithm to register MR/US prostate and rectum surfaces. Vermandel et al [12] used a hybrid approach by combining both intensity and skeleton-based methods to register 3D MRAs and 2D DSAs.…”

Section: Feature-based Methodsmentioning

confidence: 99%

Elastic Shape Analysis of Cylindrical Surfaces for 3D/2D Registration in Endometrial Tissue Characterization

Samir¹,

Kurtek²,

Srivastava³

et al. 2014

IEEE Trans. Med. Imaging

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We study the problem of joint registration and deformation analysis of endometrial tissue using 3D magnetic resonance imaging (MRI) and 2D trans-vaginal ultrasound (TVUS) measurements. In addition to the different imaging techniques involved in the two modalities, this problem is complicated due to: 1) different patient pose during MRI and TVUS observations, 2) the 3D nature of MRI and 2D nature of TVUS measurements, 3) the unknown intersecting plane for TVUS in MRI volume, and 4) the potential deformation of endometrial tissue during TVUS measurement process. Focusing on the shape of the tissue, we use expert manual segmentation of its boundaries in the two modalities and apply, with modification, recent developments in shape analysis of parametric surfaces to this problem. First, we extend the 2D TVUS curves to generalized cylindrical surfaces through replication, and then we compare them with MRI surfaces using elastic shape analysis. This shape analysis provides a simultaneous registration (optimal reparameterization) and deformation (geodesic) between any two parametrized surfaces. Specifically, it provides optimal curves on MRI surfaces that match with the original TVUS curves. This framework results in an accurate quantification and localization of the deformable endometrial cells for radiologists, and growth characterization for gynecologists and obstetricians. We present experimental results using semi-synthetic data and real data from patients to illustrate these ideas.

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Section: Feature-based Methodsmentioning

confidence: 99%

Elastic Shape Analysis of Cylindrical Surfaces for 3D/2D Registration in Endometrial Tissue Characterization

Samir¹,

Kurtek²,

Srivastava³

et al. 2014

IEEE Trans. Med. Imaging

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“…Therefore, to overcome these limitations, various semi-automatic (e.g. [26,27]), and automatic (e.g. [11][12][13]) registration strategies have been proposed.…”

Section: Us-mr Image Registrationmentioning

confidence: 99%

Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images

Yavariabdi¹,

Bartoli²,

Samir³

et al. 2015

Computerized Medical Imaging and Graphics

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We propose a new deformable slice-to-volume registration method to register a 2D Transvaginal Ultrasound (TVUS) to a 3D Magnetic Resonance (MR) volume. Our main goal is to find a cross-section of the MR volume such that the endometrial implants and their depth of infiltration can be mapped from TVUS to MR. The proposed TVUS-MR registration method uses contour to surface correspondences through a novel variational one-step deformable Iterative Closest Point (ICP) method. Specifically, we find a smooth deformation field while establishing point correspondences automatically. We demonstrate the accuracy of the proposed method by quantitative and qualitative tests on both semi-synthetic and clinical data. To generate semi-synthetic data sets, 3D surfaces are deformed with 4-40% degrees of deformation and then various intersection curves are obtained at 0-20° cutting angles. Results show an average mean square error of 5.7934±0.4615mm, average Hausdorff distance of 2.493±0.14mm, and average Dice similarity coefficient of 0.9750±0.0030.

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“…In the first category, structural points, such as corners and edges, are extracted from the two images to be registered, then point set registration methods like the Iterative Closest Point (ICP) [2] method, Gaussian Mixture Model (GMM) [3] based point set registration method and Thin Plate Spline-Robust Point Matching (TPS-RPM) [4] can align these structures and register images at the same time. However, it is difficult to extract a sufficient amount of corresponding structural points in some scenarios of the deformable registration of medical images, and the errors between the extracted corresponding points may influence the accuracy of registration.…”

Section: Introductionmentioning

confidence: 99%

Deformable multi-modal registration using 3D-FAST conditioned mutual information

Liu

Tang

Wang

et al. 2017

Computer Assisted Surgery

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Diffeomorphic surface-based registration for MR-US fusion in prostate brachytherapy

Cited by 4 publications

References 19 publications

Elastic Shape Analysis of Cylindrical Surfaces for 3D/2D Registration in Endometrial Tissue Characterization

Elastic Shape Analysis of Cylindrical Surfaces for 3D/2D Registration in Endometrial Tissue Characterization

Mapping and characterizing endometrial implants by registering 2D transvaginal ultrasound to 3D pelvic magnetic resonance images

Deformable multi-modal registration using 3D-FAST conditioned mutual information

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