Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object embedded in this space, whether this object is a curve, a surface, a structured or unstructured set of points, or any combination of them.

Deformetrica comes with three main applications:

  • registration : which computes the best possible deformation between two sets of objects.
  • atlas construction : which computes an average object configuration from a collection of object sets, and the deformations from this average to each sample in the collection.
  • geodesic regression : which computes an object time-series constrained to pass as closely as possible to a set of observations indexed by time.

Deformetrica has very little requirements about the data it can deal with.
In particular, it does not require point correspondence between objects!




If you use the software in your publications, please cite it as:

The results of this research have been obtained using the Deformetrica software [Durrleman et al. 2014].


If you would like to have your publication, which uses Deformetrica, to be listed here: us!

  • E. Bron et al, Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge, Neuroimage, 2015 (in press) link
  • A. B. G. Fouquier, S. Durrleman, J. Yelnik, S. Fernandez-Vidal, E. Bardinet, 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), 2014, pdf
  • A. Routier, P. Gori, A. B. Graciano Fouquier, S. Lecomte, O. Colliot, S. Durrleman, 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), 2014, pdf
  • P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, C. Poupon, A. Hartmann, N. Ayache, S. Durrleman
    A Bayesian Framework for Joint Morphometry of Surface and Curve meshes in Multi-Object Complexes
    Medical Image Analysis, 35, pp.458-474, 2017, pdf
  • P. Gori, O. Colliot, L. Marrakchi-Kacem, Y. Worbe, A. Routier, C. Poupon, A. Hartmann, N. Ayache, S. Durrleman
    Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms
    IPMI, Springer (LNCS), 2015   (Oral - Acceptance rate: ~5%), pdf


Stanley Durrleman

  • Project founder and coordinator

Pietro Gori

  • Bayesian Atlas

 Ana Beatriz Graciano Fouquier

  • 2D&3D Images classes
  • Handling for images & meshes

  • Regression
  • Bayesian Atlas Mixture
  • McmcSaem
  • Longitudinal Atlas

  • Parallel Transport
  • Utilities
  • Examples

Michael Bacci

  • Software Maintainer/Architecture
  • Linear Algebra
  • Optimizations
  • I/O
  • CMake/Docker/Libraries
  • Test/Benchmarks/Profiling

  • Former Software Maintainer/Architecture
  • Linear Algebra
  • GPU integration
  • Documentation

Benjamin Charlier

  • CUDA
  • Debugger expert
  • Unit and Functional tests
  • Supervision of math algorithms development

James Fishbaugh

  • Geodesic Regression

Marcel Prastawa

  • Project founder
  • Initial software architect
  • 2D&3D Images classes

Cédric Doucet

  • Linear Algebra
  • Optimizations

Mauricio Diaz

  • Continuous Integration