ArticleMedical image analysis2017
Geodesic shape regression with multiple geometries and sparse parameters.
Article in Medical image analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Numerical validation of the applicability of the simplified ventricular model in the analysis of hemolysis in the mitral paravalvular leak.Frontiers in bioengineering and biotechnology · 2025Article
- Shape modeling of longitudinal medical images: from diffeomorphic metric mapping to deep learning.Frontiers in artificial intelligence · 2025Review
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- MRI-based axis-referenced morphometric model corresponding to lamellar organization for assessing hippocampal atrophy in dementia.Human brain mapping · 2024Article
- Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.Scientific reports · 2023Article
- Morphological Variation in the Pelvic Floor Muscle Complex of Nulliparous, Pregnant, and Parous Women.Annals of biomedical engineering · 2023Article
- CLAIRE-Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications.Journal of imaging · 2022Article
- Predictive Statistical Model of Early Cranial Development.IEEE transactions on bio-medical engineering · 2022Article
- Geodesic density regression for correcting 4DCT pulmonary respiratory motion artifacts.Medical image analysis · 2021Article
- Fast GPU 3D diffeomorphic image registration.Journal of parallel and distributed computing · 2021Article
- Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems.International Conference for High Performance Computing, Networking, Storage and Analysis : [proceedings]. SC (Conference : Supercomputing) · 2020Article
- Model selection for spatiotemporal modeling of early childhood sub-cortical development.Proceedings of SPIE--the International Society for Optical Engineering · 2019Article
- CLAIRE: A DISTRIBUTED-MEMORY SOLVER FOR CONSTRAINED LARGE DEFORMATION DIFFEOMORPHIC IMAGE REGISTRATION.SIAM journal on scientific computing : a publication of the Society for Industrial and Applied Mathematics · 2019Article
- ESTIMATING SHAPE CORRESPONDENCE FOR POPULATIONS OF OBJECTS WITH COMPLEX TOPOLOGY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
- 4D CONTINUOUS MEDIAL REPRESENTATION BY GEODESIC SHAPE REGRESSION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2018Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Many problems in medicine are inherently dynamic processes which include the aspect of change over time, such as childhood development, aging, and disease progression. From medical images, numerous geometric structures can be extracted with various representations, such as landmarks, point clouds, curves, and surfaces. Different sources of geometry may characterize different aspects of the anatomy, such as fiber tracts from DTI and subcortical shapes from structural MRI, and therefore require a modeling scheme which can include various shape representations in any combination. In this paper, we present a geodesic regression model in the large deformation (LDDMM) framework applicable to multi-object complexes in a variety of shape representations. Our model decouples the deformation parameters from the specific shape representations, allowing the complexity of the model to reflect the nature of the shape changes, rather than the sampling of the data. As a consequence, the sparse representation of diffeomorphic flow allows for the straightforward embedding of a variety of geometry in different combinations, which all contribute towards the estimation of a single deformation of the ambient space. Additionally, the sparse representation along with the geodesic constraint results in a compact statistical model of shape change by a small number of parameters defined by the user. Experimental validation on multi-object complexes demonstrate robust model estimation across a variety of parameter settings. We further demonstrate the utility of our method to support the analysis of derived shape features, such as volume, and explore shape model extrapolation. Our method is freely available in the software package deformetrica which can be downloaded at www.deformetrica.org.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.