DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting

By Yangming Ou, Aristeidis Sotiras, Nikos Paragios, Christos Davatzikos. Information Processing in Medical Imaging, 2009

386 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b3ff39378175d30ba167adec5f1847bca01bbff4

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Citations in the corpus

MSM: a new flexible framework for Multimodal Surface Matching☆ (2014, neutral): “A major motivation for this choice is the flexibility it offers in the selection of similarity measures (Glocker et al., 2008; Kwon et al., 2011; Ou et al., 2011), as it is not currently clear how different features covary across subjects, nor what set of features is the most optimal for driving…”
Harmonization of multi-site diffusion tensor imaging data (2017, methodology): “2.3.5 ComBat The ComBat model was introduced in the context of gene expression analysis by Johnson et al. [2007] as an improvement of location/scale models [Parmigiani et al., 2003] for studies with small sample size.”
Deformable Medical Image Registration: A Survey (2013, methodology): “[323] optimized the Gabor features to be more distinctive and employed the notion of mutual saliency to let the most reliable points drive the registration process.”
Multi-Atlas Segmentation of Biomedical Images: A Survey (2014, methodology): “Typically, one independent registration is computed between each atlas and the novel image and generic intensitybased registration tools, such as (Avants et al., 2009; Klein et al., 2010; Rueckert et al., 1999; Ou et al., 2011), are used.”
Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks (2020, background): “etween the images. Deformable registration is crucial in a variety of medical image analysis, including diagnostic tasks, radiotherapy and imageguided surgery. Conventional image registration methods [23,18,2,24] often rely on the multi-resolution strategy and estimate the target transformation iter”
MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration (2012, background): “if this assumption is violated the concept of mutual-saliency 750 (Ou et al. (2011)) could be incorporated to improve the robust751 ness in these cases.”
Deformable Image Registration Using Cue-aware Deep Regression Network (2018, methodology): “Commonly-used methods include AIR [23], ART [26], ANTS [34], Demons [20], Diffeomorphic Demons [21], [39], SPM [40], RAMMS [36], DROP [41], CC/MI/SSD-FFD [35], and FNIRT [22], etc .”
Deep Learning in Medical Image Analysis (2017, background): “Noticeably, it is very difficult to learn meaningful features given such inaccurate correspondences derived from imperfect image registration, as suffered by many supervised learning methods (79, 80, 81).”
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013, methodology): “First, the DRAMMS image registration algorithm is used (Ou et al., 2010).”
Understanding and Interpreting Machine Learning in Medical Image Computing Applications (2018, methodology): “Consequently several attempts have been made to model the disease evolution in a data-driven way, using sets of biomarkers extracted from different imaging acquisition techniques, such as Magnetic Resonance Imaging (MRI) [12].”
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection (2015, neutral): “We further included g = 0.1 for DRAMMS and s = 0.25 for ANTs, which means smaller weights for smoothness.”
A review of multimodal image matching: Methods and applications (2021, neutral): “Gabor filtering can easily capture texture information from raw images, which is why it is widely used for modality unification [364,366], as also conducted by several local frequency representations [365].”
Neuroimaging of the Philadelphia Neurodevelopmental Cohort (2013, positive): “Highly accurate registration of T1-weighted structural images to template space was achieved using DRAMMS (Ou et al., 2011).”
Survey of Non-Rigid Registration Tools in Medicine (2017, methodology): “4 bUnwarpJ MSD Inverse consistent B-spline [60] 5 DRAMMS CC, SSD Deformable registration via attribute matching and mutual-saliency weighting [62] 6 Drop SAD, SADG, SSD, NCC, NMI, CR, CCGIP, HD, JRD, MI, JE, GRAD Deformable image registration based on Markov random fields and discrete optimization [”
An unsupervised convolutional neural network-based algorithm for deformable image registration (2018, background): “Another group of supervised learning strategies focuses on selecting an optimal set of features from a large feature pool (Wu et al 2006, Ou et al 2011).”