A multi-scale kernel bundle for LDDMM: towards sparse deformation description across space and scales
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A multi-scale kernel bundle for LDDMM : towards sparse deformation description across space and scales. / Sommer, Stefan Horst; Nielsen, Mads; Lauze, Francois Bernard; Pennec, Xavier.
Information Processing in Medical Imaging: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings. red. / Gábor Székely; Horst K. Hahn. Springer, 2011. s. 624-35 (Lecture notes in computer science, Bind 6801).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - A multi-scale kernel bundle for LDDMM
AU - Sommer, Stefan Horst
AU - Nielsen, Mads
AU - Lauze, Francois Bernard
AU - Pennec, Xavier
N1 - Conference code: 22
PY - 2011
Y1 - 2011
N2 - The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations. This paper presents an extension of the LDDMM framework allowing multiple kernels at multiple scales to be incorporated in each registration while preserving many of the mathematical properties of standard LDDMM. On a dataset of landmarks from lung CT images, we show by example the influence of the kernel size in standard LDDMM, and we demonstrate how our framework, LDDKBM, automatically incorporates the advantages of each scale to reach the same accuracy as the standard method optimally tuned with respect to scale. The framework, which is not limited to landmark data, thus removes the need for classical scale selection. Moreover, by decoupling the momentum across scales, it promises to provide better interpolation properties, to allow sparse descriptions of the total deformation, to remove the tradeoff between match quality and regularity, and to allow for momentum based statistics using scale information.
AB - The Large Deformation Diffeomorphic Metric Mapping framework constitutes a widely used and mathematically well-founded setup for registration in medical imaging. At its heart lies the notion of the regularization kernel, and the choice of kernel greatly affects the results of registrations. This paper presents an extension of the LDDMM framework allowing multiple kernels at multiple scales to be incorporated in each registration while preserving many of the mathematical properties of standard LDDMM. On a dataset of landmarks from lung CT images, we show by example the influence of the kernel size in standard LDDMM, and we demonstrate how our framework, LDDKBM, automatically incorporates the advantages of each scale to reach the same accuracy as the standard method optimally tuned with respect to scale. The framework, which is not limited to landmark data, thus removes the need for classical scale selection. Moreover, by decoupling the momentum across scales, it promises to provide better interpolation properties, to allow sparse descriptions of the total deformation, to remove the tradeoff between match quality and regularity, and to allow for momentum based statistics using scale information.
U2 - 10.1007/978-3-642-22092-0_51
DO - 10.1007/978-3-642-22092-0_51
M3 - Article in proceedings
SN - 978-3-642-22091-3
T3 - Lecture notes in computer science
SP - 624
EP - 635
BT - Information Processing in Medical Imaging
A2 - Székely, Gábor
A2 - Hahn, Horst K.
PB - Springer
Y2 - 3 July 2011 through 8 July 2011
ER -
ID: 170211210