Skip to Main content Skip to Navigation
New interface
Journal articles

Probabilistic Atlas and Geometric Variability Estimation to Drive Tissue Segmentation

Abstract : Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, that presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modelled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets.
Document type :
Journal articles
Complete list of metadata

Cited literature [21 references]  Display  Hide  Download
Contributor : Bertrand Thirion Connect in order to contact the contributor
Submitted on : Monday, January 5, 2015 - 10:32:44 PM
Last modification on : Saturday, June 25, 2022 - 9:09:18 PM
Long-term archiving on: : Wednesday, June 3, 2015 - 5:06:39 PM


Files produced by the author(s)




Hao Xu, Bertrand Thirion, Stéphanie Allassonnière. Probabilistic Atlas and Geometric Variability Estimation to Drive Tissue Segmentation. Statistics in Medicine, 2014, 33 (20), pp.24. ⟨10.1002/sim.6156⟩. ⟨hal-01094739⟩



Record views


Files downloads