citations

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  • [Bône et al. 2020], Learning the spatiotemporal variability in longitudinal shape data sets, International Journal of Computer Vision
  • [Bône et al. 2019], Hierarchical modeling of Alzheimer’s disease progression from a large longitudinal MRI data set, Organization for Human Brain Mapping Annual Meeting
  • [Bône et al. 2018b]Deformetrica 4: an open-source software for statistical shape analysis, International Workshop on Shape in Medical Imaging
  • [Bône et al. 2018a]Learning distributions of shape trajectories from longitudinal datasets: a hierarchical model on a manifold of diffeomorphisms, International Conference on Computer Vision and Pattern Recognition
  • [Biffi et al. 2017]Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis, Frontiers in Pediatrics
  • [Bône et al. 2017]Prediction of the Progression of Subcortical Brain Structures in Alzheimer’s Disease from Baseline, MICCAI Workshop on Mathematical Foundations of Computational Anatomy
  • [Bruse et al. 2017]Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches, IEEE Transactions on Biomedical Engineering
  • [Bruse et al. 2017], How successful is successful? Aortic arch shape after successful aortic coarctation repair correlates with left ventricular function, The Journal of Thoracic and Cardiovascular Surgery
  • [Fishbaugh et al. 2017], A Geodesic shape regression with multiple geometries and sparse parameters, Medical Image Analysis
  • [Gori et al. 2017], A Bayesian Framework for Joint Morphometry of Surface and Curve meshes in Multi-Object Complexes, Medical Image Analysis
  • [Louis et al. 2017], Parallel transport in shape analysis: a scalable numerical scheme, Geometric Science of Information
  • [Bruse et al. 2016], Looks Do Matter! Aortic Arch Shape After Hypoplastic Left Heart Syndrome Palliation Correlates With Cavopulmonary Outcomes, Annual Meeting of The Society of Thoracic Surgeons
  • [Tenhagen et al. 2016], Three-Dimensional Handheld Scanning to Quantify Head-Shape Changes in Spring-Assisted Surgery for Sagittal Craniosynostosis, Journal of Craniofacial Surgery
  • [Bruse et al. 2016], A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta, BMC Medical Imaging
  • [Bron et al. 2015], Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge, Neuroimage
  • [Bruse et al. 2015], CMR-based 3D statistical shape modelling reveals left ventricular morphological differences between healthy controls and arterial switch operation survivors, J Cardiovasc Magn Reson
  • [Bruse et al. 2015], A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?, Statistical Atlases and Computational Modeling of the Heart
  • [Gori et al. 2015]Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms, IPMI
  • [Fouquier et al. 2014], Iconic-Geometric Nonlinear Registration of a Basal Ganglia Atlas for Deep Brain Stimulation Planning, In Proc. of MICCAI Workshop on Deep Brain Stimulation Methodological Challenges (DBSMC’14)
  • [Routier et al. 2014], Evaluation of morphometric descriptors of deep brain structures for the automatic classification of patients with Alzheimer’s disease, mild cognitive impairment and elderly controls, In MICCAI challenge on Computer-Aided Diagnosis of Dementia based on structural MRI data (CADDementia)
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