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Probabilistic atlas and geometric variability estimation to drive tissue segmentation
Author(s) -
Xu Hao,
Thirion Bertrand,
Allassonnière Stéphanie
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6156
Subject(s) - atlas (anatomy) , computer science , artificial intelligence , probabilistic logic , segmentation , population , statistical model , pattern recognition (psychology) , generative model , metric (unit) , image registration , medical imaging , computer vision , image (mathematics) , generative grammar , anatomy , medicine , operations management , demography , sociology , economics
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, which 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 modeled 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. Copyright © 2014 John Wiley & Sons, Ltd.