elastix: A Toolbox for Intensity-Based Medical Image Registration

By Stefan Klein, Marius Staring, Keelin Murphy, Max A. Viergever, Josien P. W. Pluim. IEEE Transactions on Medical Imaging, 2010

4,197 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f900f21f960b334907d1d54783927922fc949c98

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Mapping of brain activity by automated volume analysis of immediate early genes (2016, positive): “The ClearMap pipeline includes interfaces to the Elastix open-source software (Klein et al., 2010; Shamonin et al., 2014) to automatically register the iDISCO+ light sheet scans, as previously developed for STP imaged mouse brains (Kim et al., 2015).”
ITK: enabling reproducible research and open science (2014, positive): “…for a number of open source analysis tools; a small sampling includes 3D Slicer (Fedorov et al., 2012), ANTS (Tustison et al., 2009), Elastix (Klein et al., 2010), ITK-SNAP (ITK-SNAP, 2014), MITK (MITK, 2014), OsiriX (OsiriX, 2014), Seg3D (CIBC, 2014), Vaa3D (V3D, 2014), Voreen (Voreen,…”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “Typically, one registration is computed between each atlas and the novel image and generic intensity-based registration tools, such as (Avants et al., 2009; Klein et al., 2010; Rueckert et al., 1999), are used.”
Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019, methodology): “Besides, we also compare against SyN [4] (integrated in ANTs [5] together with the afﬁne stage) and B-spline [48] (integrated in Elastix [35] together with the afﬁne stage), which are shown to be the top-performing traditional meth-ods for deformable image registration [8, 34, 37].”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, methodology): “The follow-up evaluation study Klein et al. (2010a) evaluated ART2.”
Robust Non-rigid Registration Through Agent-Based Action Learning (2017, methodology): “D using the elastix -framework[3] with standard parameters followed by cropping and down sampling the images (to 100x100/75x75x20 pixels in 2-”
Partial volume effect as a hidden covariate in DTI analyses (2011, methodology): “(non-diffusion weighted images) with elastix (Klein et al., 2010) using an affine coregistration technique (12 degrees of freedom) with mutual information as the cost function (Pluim et al.”
Dixon-VIBE Deep Learning (DIVIDE) Pseudo-CT Synthesis for Pelvis PET/MR Attenuation Correction (2018, methodology): “Then, intrasubject rigid and nonrigid registration of the MR and CT data was performed using SPM8 software ( 28 ) and the Elastix package ( 29 ).”
The Insight ToolKit image registration framework (2014, methodology): “Thus, these methods are within the leading ranks of image registration methodologies as evaluated in recent work as well as in the more traditional brain (Klein et al., 2010) and lung CT (Murphy et al., 2011) studies.”
Intensity-Based Image Registration by Minimizing Residual Complexity (2010, methodology): “We compare our similarity measure with the local CC and local MI similarity measures implemented in Elastix [15], [25].”
CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration (2020, methodology): “…Methods: In order to verify the improved performance of the proposed method, we employed several comparative methods that show the state-of-the-art performance in the image registration: Elastix [8], SyN [7] by Advanced Normalization Tools (ANTs) [36], VoxelMorph [16], and MS-DIRNet [18].”
Contrastive learning of global and local features for medical image segmentation with limited annotations (2020, positive): “Moreover, such images, especially when they are acquired with the same modality and capture the same ﬁeld-of-view, can be roughly aligned relatively easily using linear registration, for instance as implemented in Elastix [36].”
Survey of Non-Rigid Registration Tools in Medicine (2017, neutral): “Elastix [64] was developed at the Image Sciences Institute, University Medical Center Utrecht, The Netherlands, by Stefan Klein, Marius Staring, and Josien Pluim.”
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018, methodology): “The sequences were aligned by rigid registration using Elastix [57], along with a bias correction performed using SPM8 [58].”
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease (2013, methodology): “Parallelization is performed in the context of the image registration software elastix (Klein et al., 2010), available at http:// elastix . isi . uu . nl.”