Abstract. This paper introduces a new similarity measure designed to bring a population of segmented subjects into alignment in a common coordinate system. Our metric aligns each subject with a hidden probabilistic model of the common spatial distribution of anatomical tissues, estimated using STAPLE. Our approach does not require the selection of a subject of the population as a "target subject", nor the identification of "stable" landmarks across subjects. Rather, the approach determines automatically from the data what the most consistent alignment of the joint data is, subject to the particular transformation family used to align the subjects. The computational cost of joint simultaneous registration of the population of subjects is small due to the use of an efficient gradient estimate used to solve the optimization transform aligning each subject. The efficacy of the approach in constructing an unbiased statistical atlas was demonstrated by carrying out joint alignment of 20 segmentations of MRI of healthy preterm infants, using an affine transformation model and a FEM volumetric tetrahedral mesh transformation model.

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Delineation of structures to irradiate (the tumors) as well as structures to be spared (e.g., optic nerve, brainstem, or eyes) is required for advanced radiotherapy techniques. Due to a lack of time and the number of patients to be treated these cannot always be segmented accurately which may lead to suboptimal plans. A possible solution is to develop methods to identify these structures automatically. This study tests the hypothesis that a fully automatic, atlas-based segmentation method can be used to segment most brain structures needed for radiotherapy plans even tough tumors may deform normal anatomy substantially. This is accomplished by registering an atlas with a subject volume using a combination of rigid and non-rigid registration algorithms. Segmented structures in the atlas volume are then mapped to the corresponding structures in the subject volume using the computed transformations. The method we propose has been tested on two sets of data, i.e., adults and children/young adults. For the first set of data, contours obtained automatically have been compared to contours delineated manually by three physicians. For the other set qualitative results are presented.

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