In this paper, we propose a new algorithm to estimate diffeomorphic organ atlases out of 3D medical images. More precisely, we explore the feasibility of Kärcher means by using large deformations by diffeomorphisms (LDDMM). This framework preserves organs topology and has interesting properties to quantitatively describe their anatomical variability. We also use a new registration algorithm based on an optimal control method to satisfy the geodesicity of the deformations at any step of the optimisation process. Initial tangent vectors to the shapes, which are used to compute the Kärcher mean, are therefore estimated accurately. Our methodology is tested on different groups of 3D images representing organs with a large anatomical var...
Diffeomorphisms have received significant research focus in the medical image registration community...
In the framework of large deformation diffeomorphic metric mapping (LDDMM), we present a practical m...
© Springer International Publishing AG 2017. This paper presents an efficient algorithm for large de...
An important question, historically, is how structure and function relate. The field of medical imag...
An important question, historically, is how structure and function relate. The field of medical imag...
In this paper we present two fine and coarse approaches for the efficient registration of 3D medical...
International audienceThis paper introduces a variational strategy to learn spatially-varying metric...
International audienceThis paper introduces a variational strategy to learn spatially-varying metric...
In the context of large deformations by diffeomorphisms, we propose a new diffeomorphic registration...
International audienceThis paper introduces a variational strategy to learn spatially-varying metric...
The construction of average models of anatomy, as well as regression analysis of anatomical structur...
The construction of average models of anatomy, as well as regression analysis of anatomical structur...
This paper presents a novel approach for diffeomorphic image regression and atlas estimation that re...
In this paper, we present a fine and coarse approach for the multiscale registration of 3D medical i...
journal articleThe construction of population atlases is a key issue in medical image analysis, and ...
Diffeomorphisms have received significant research focus in the medical image registration community...
In the framework of large deformation diffeomorphic metric mapping (LDDMM), we present a practical m...
© Springer International Publishing AG 2017. This paper presents an efficient algorithm for large de...
An important question, historically, is how structure and function relate. The field of medical imag...
An important question, historically, is how structure and function relate. The field of medical imag...
In this paper we present two fine and coarse approaches for the efficient registration of 3D medical...
International audienceThis paper introduces a variational strategy to learn spatially-varying metric...
International audienceThis paper introduces a variational strategy to learn spatially-varying metric...
In the context of large deformations by diffeomorphisms, we propose a new diffeomorphic registration...
International audienceThis paper introduces a variational strategy to learn spatially-varying metric...
The construction of average models of anatomy, as well as regression analysis of anatomical structur...
The construction of average models of anatomy, as well as regression analysis of anatomical structur...
This paper presents a novel approach for diffeomorphic image regression and atlas estimation that re...
In this paper, we present a fine and coarse approach for the multiscale registration of 3D medical i...
journal articleThe construction of population atlases is a key issue in medical image analysis, and ...
Diffeomorphisms have received significant research focus in the medical image registration community...
In the framework of large deformation diffeomorphic metric mapping (LDDMM), we present a practical m...
© Springer International Publishing AG 2017. This paper presents an efficient algorithm for large de...