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Conference Proceedings

Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks. S.Estrada,...M.Reuter. MICCAI 2018 Workshop. [arXiv].
Complex Fully Convolutional Neural Networks for MR Image Reconstruction. M.Dedmari,...M.Reuter. MICCAI 2018 Workshop. [arXiv].
Latent Processes Governing Neuroanatomical Change in Aging and Dementia. C.Wachinger, A.Rieckmann, M.Reuter. MICCAI 2017.
Mid-space-independent symmetric data term for pairwise deformable image registration. I.Aganj, et al.. MICCAI, 2015 [PDF] [DOI-Link].
BrainPrint: Identifying Subjects by their Brain. C.Wachinger, P.Golland, M.Reuter. In Proc MICCAI, LNCS 8675:41-48, 2014. [PDF] [BibTex] [DOI-Link] [PubMed]
BrainPrint in the Computer-Aided Diagnosis of Alzheimer's Disease. C.Wachinger, ..., M.Reuter. In Proc MICCAI Challenge on Computer-Aided Diagnosis of Dementia Based on Structural MRI Data p.129-138, 2014. [PDF] [BibTex]
Gaussian Process interpolation for uncertainty estimation in image registration. C.Wachinger, P.Golland, M.Reuter, W.Wells. In Proc MICCAI, 17(Pt 1):267-274, 2014.
Symmetric Non-Rigid Image Registration via an Adaptive Quasi-Volume-Preserving Constraint. I.Aganj, M.Reuter, et al. In Proc. of the Int. Symp. Biomedical Imaging 2013, San Francisco. [PDF].
Longitudinal FreeSurfer for Reliable Imaging Biomarkers. M.Reuter, HD.Rosas, B.Fischl. MICCAI NIBAD'12 Challenge, 12 pages, Nice, 2012. [PDF] [BibTex] details

We have presented an unbiased longitudinal processing framework previously. Here we discuss an extension to improve results in subjects with large ventricles. Furthermore, we demonstrate methods to investigate longitudinal data via scatter plots, as well as linear models and non-linear flow lines. Using normalized brain volume to estimate disease progression, we find increased atrophy rates in several structures in advanced disease stages. We also highlight how linear fits into percent volume changes can support QA and detection of early disease effects. Finally we show improved surface placement accuracy when using longitudinal image processing in cases with low image quality relative to independent processing. hide

SHREC’11 Track: Shape Retrieval on Non-rigid 3D Watertight Meshes. Z.Lian, A.Godil et al. In Proceedings of the Eurographics Workshop on 3D Object Retrieval, pp. 79-88, 2011. [PDF] [BibTex] [EG Pub] [SHREC'11-NonRigid] details

Non-rigid 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of non-rigid 3D shape retrieval methods implemented by different participants around the world. The track is based on a new non-rigid 3D shape benchmark, which contains 600 watertight triangle meshes that are equally classified into 30 categories. In this track, 25 runs have been submitted by 9 groups and their retrieval accuracies were evaluated using 6 commonly-utilized measures. Our method ShapeDNA (introduced at SPM in 2005, see below) has reached the second place together with a method by Lian et al., who also organized this competition. The first place was reached by Smeets et al. hide

Global Medical Shape Analysis Using the Volumetric Laplace Spectrum. M. Reuter, M. Niethammer, F.-E. Wolter, S. Bouix, M. Shenton. Proceedings of the 2007 Int. Conf. on Cyberworlds, NASA-GEM Workshop, IEEE Computer Society, pp.417-426, 2007. [PDF] [BibTex] [DOI-Link] details

This paper proposes to use the volumetric Laplace spectrum as a global shape descriptor for medical shape analysis. The approach allows for shape comparisons using minimal shape preprocessing. In particular, no registration, mapping, or remeshing is necessary. All computations can be performed directly on the voxel representations of the shapes. The discriminatory power of the method is tested on a population of female caudate shapes (subcortical gray matter structure of the brain, involved in memory function, emotion processing, and learning) of normal control subjects and of subjects with schizotypal personality disorder. The behavior and properties of the volumetric Laplace spectrum are discussed extensively for both the Dirichlet and Neumann boundary condition showing advantages of the Neumann spectra. Both, the computations of spectra on 3D voxel data for the purpose of shape matching as well as the use of the Neumann spectrum for shape analysis are completely new. hide

Global Medical Shape Analysis using the Laplace-Beltrami Spectrum. M. Niethammer, M. Reuter, F.-E. Wolter, S. Bouix, N. Peinecke, M.-S. Koo, M. Shenton. MICCAI07, 10th International Conference on Medical Image Computing and Computer Assisted Intervention, Part I, LNCS 4791, pp. 850-857, 2007. [PDF] [BibTex] [DOI-Link] [PubMed] details

This paper proposes to use the Laplace-Beltrami spectrum (LBS) as a global shape descriptor for medical shape analysis. The approach allows for shape comparisons using minimal shape preprocessing. In particular, no registration, mapping, or remeshing is necessary. The discriminatory power of the method is tested on a population of female caudate shapes of normal control subjects and of subjects with schizotypal personality disorder. hide

Can one hear Shape?. M. Reuter. PAMM Proceedings of GAMM07 and ICIAM07, Vol 7, Issue 1, 6th International Congress of Industrial and Applied Mathematics, SIAM, October 2008. [PDF] [BibTex] [DOI-Link] details

The question "Can one hear the shape of a drum" has been asked in several contexts before (e.g., by Bers and Kac). It is a pictorial way of asking if the eigenvalues of the Laplacian on a given domain completely characterize its shape, in other words, if the spectrum is a complete shape descriptor (which it is not in general). In this talk we will give an overview on how the computation of the spectra can be accomplished using FEM for manifolds in 2D and 3D (e.g. iso-surfaces, boundary representations, solid bodies, vector fields...) with the Dirichlet and Neumann boundary condition. We demonstrate that it is computational feasible to numerically extract geometric properties (volume, area, boundary length and even the Euler characteristic) from the first eigenvalues. Since the spectrum contains geometrical information and since it is an isometry invariant and therefore independent of the object's representation, parametrization, spatial position, and optionally of its size, it is optimally suited to be used as a fingerprint (Shape-DNA) in contemporary computer graphics applications like database retrieval, quality assessment, and shape matching in fields like CAD, medicine or engineering. hide

Laplace-Spectra as Fingerprints for Shape Matching. M. Reuter, F.-E. Wolter and N. Peinecke. Proceedings of the ACM Symposium on Solid and Physical Modeling, pp.101-106, June 2005. [PDF] [BibTex] [DOI-Link] details

This paper introduces a method to extract fingerprints of any surface or solid object by taking the eigenvalues of its respective Laplace-Beltrami operator. Using an object's spectrum (i.e. the family of its eigenvalues) as a fingerprint for its shape is motivated by the fact that the related eigenvalues are isometry invariants of the object. Employing the Laplace-Beltrami spectra (not the spectra of the mesh Laplacian) as fingerprints of surfaces and solids is a novel approach in the field of geometric modeling and computer graphics. Those spectra can be calculated for any representation of the geometric object (e.g. NURBS or any parametrized or implicitly represented surface or even for polyhedra). Since the spectrum is an isometry invariant of the respective object this fingerprint is also independent of the spatial position. Additionally the eigenvalues can be normalized so that scaling factors for the geometric object can be obtained easily. Therefore checking if two objects are isometric needs no prior alignment (registration / localization) of the objects, but only a comparison of their spectra. With the help of such fingerprints it is possible to support copyright protection, database retrieval and quality assessment of digital data representing surfaces and solids. hide
Martin Reuter - MIT - Cambridge, MA, USA - EMail:
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