A method for the 3D reconstruction of an anatomical structure, the method comprising: - obtaining at least two calibrated 2D bi-planar images of the structure; - associating with each pixel of each of the at least two calibrated 2D bi-planar images a textural vector characterizing the distribution of the gradient texture and orientation in a window centered around the pixel; - applying a non-linear statistical dimensionality reduction procedure to the textural vectors to obtain reduced dimension textural vectors; - creating segmented images of the at least two calibrated 2D bi-planar images using the reduced dimension textural vectors; - selecting a representative 3D model of the anatomical structure from a learning set containing a plurality of structure surface models, wherein the selection of the representative 3D model of the structure is performed by: - associating with each structure surface model a shape vector characterizing the shape of the structure surface model in 3D; - applying a non-linear statistical dimensionality reduction procedure to the shape vectors to obtain reduced dimension shape vectors; - selecting a point in the reduced dimension space; - varying the reduced dimension shape vector corresponding to the selected point by a predefined interval; - selecting a predefined number of closest neighboring points to the selected point; and - providing the representative 3D model by interpolation using the shape vectors associated with the closest neighboring points to the selected point; the predefined number of closest neighboring points being a function of the degree of the interpolation. - projecting the representative 3D model onto the segmented images; and - adjusting the projection with the contours and homogeneous zones of the segmented images by minimizing a cost function of the deformation parameters of the representative 3D model to obtain the 3D reconstruction of the anatomical structure. - obtaining at least two calibrated 2D bi-planar images of the structure; - associating with each pixel of each of the at least two calibrated 2D bi-planar images a textural vector characterizing the distribution of the gradient texture and orientation in a window centered around the pixel; - applying a non-linear statistical dimensionality reduction procedure to the textural vectors to obtain reduced dimension textural vectors; - creating segmented images of the at least two calibrated 2D bi-planar images using the reduced dimension textural vectors; - selecting a representative 3D model of the anatomical structure from a learning set containing a plurality of structure surface models, wherein the selection of the representative 3D model of the structure is performed by: - associating with each structure surface model a shape vector characterizing the shape of the structure surface model in 3D; - applying a non-linear statistical dimensionality reduction procedure to the shape vectors to obtain reduced dimension shape vectors; - selecting a point in the reduced dimension space; - varying the reduced dimension shape vector corresponding to the selected point by a predefined interval; - selecting a predefined number of closest neighboring points to the selected point; and - providing the representative 3D model by interpolation using the shape vectors associated with the closest neighboring points to the selected point; the predefined number of closest neighboring points being a function of the degree of the interpolation. - associating with each structure surface model a shape vector characterizing the shape of the structure surface model in 3D; - applying a non-linear statistical dimensionality reduction procedure to the shape vectors to obtain reduced dimension shape vectors; - selecting a point in the reduced dimension space; - varying the reduced dimension shape vector corresponding to the selected point by a predefined interval; - selecting a predefined number of closest neighboring points to the selected point; and - providing the representative 3D model by interpolation using the shape vectors associated with the closest neighboring points to the selected point; - projecting the representative 3D model onto the segmented images; and - adjusting the projection with the contours and homogeneous zones of the segmented images by minimizing a cost function of the deformation parameters of the representative 3D model to obtain the 3D reconstruction of the anatomical structure.