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Geometrical growth models for computational anatomy
le 3 octobre 2016
10h00
Thèse de doctorat d'Irène Kaltenmark (CMLA)
irene_kaltenmark.jpg
However, the analysis of medical imaging data also requires the processing of more complex changes, which especially appear during growth or aging phenomena. The observed organisms are subject to transformations over time that are no longer diffeomorphic, at least in a biological sense. One reason might be a gradual creation of new material uncorrelated to the preexisting one. The evolution of the shape can then be described by the joint action of a deformation process and a creation process.
For this purpose, we offer to extend the LDDMM framework to address the problem of non diffeomorphic structural variations in longitudinal data. We keep the geometric central concept of a group of deformations acting on embedded shapes. The necessity
for partial mappings leads to a time-varying dynamic that modifies the action of the group of deformations.
Ultimately, growth priors are integrated into a new optimal control problem for assimilation of time-varying surface data, leading to an interesting problem in the field of the calculus of variations where the choice of the attachment term on the data, current or varifold, plays an unexpected role.
- Type :
- Thèses - HDR
- Lieu(x) :
- Campus de Cachan
Bâtiment Laplace, Salle Renaudeau, R-de-Ch.
Tutelle
Jury de thèse
Advisor
Alain Trouvé (CMLA, ENS Cachan)
Examiner
Mohamed Daoudi
(Institut Mines Telecom Lille)
Joan Glaunes (université Paris Descartes)
Jean-François Mangin (CEA Neurospin)
Referees
Peter Michor (université de Vienne)
Gabriel Peyré (université Paris Dauphine)
Alain Trouvé (CMLA, ENS Cachan)
Examiner
Mohamed Daoudi
(Institut Mines Telecom Lille)
Joan Glaunes (université Paris Descartes)
Jean-François Mangin (CEA Neurospin)
Referees
Peter Michor (université de Vienne)
Gabriel Peyré (université Paris Dauphine)