ReviewInternational journal of computer assisted radiology and surgery2016
Entropy-based particle correspondence for shape populations.
Review in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed.
- HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing.Computer methods in applied mechanics and engineering · 2026Article
- Feasibility study for the automatic surgical planning method based on statistical model.Journal of orthopaedic surgery and research · 2023Article
- Enabling supra-aortic vessels inclusion in statistical shape models of the aorta: a novel non-rigid registration method.Frontiers in physiology · 2023Article
- Benchmarking off-the-shelf statistical shape modeling tools in clinical applications.Medical image analysis · 2022Article
- Establishing Surface Correspondence for Post-surgical Cortical Thickness Changes in Temporal Lobe Epilepsy.Proceedings of SPIE--the International Society for Optical Engineering · 2021Article
- Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization.Nature communications · 2019Article
- Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
- On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application.Shape in medical imaging : International Workshop, ShapeMI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018 : proceedings. ShapeMI (Workshop) (2018 : Granada, Spain) · 2018Article
- Genetic mapping of molar size relations identifies inhibitory locus for third molars in mice.Heredity · 2018Article
- Hierarchical Vertex Regression-Based Segmentation of Head and Neck CT Images for Radiotherapy Planning.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2018Article
- Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy.Biomedical engineering online · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposeStatistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies.
methodsIn this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics.
resultsWe apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques.
conclusionsThrough the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.
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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.