Average brain models: a convergence study
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
Unbiased atlas formation via large deformations metric mapping
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A log-euclidean framework for statistics on diffeomorphisms
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Assessing selection methods in the context of multi-atlas based segmentation
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Construction of patient specific atlases from locally most similar anatomical pieces
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
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Radiotherapy planning needs accurate delineations of the critical structures. Atlas-based segmentation has been shown to be very efficient to delineate brain structures [1]. However, the construction of an atlas from a dataset of images [2], particularly for the head and neck region, is very difficult due to the high variability of the images and can generate over-segmented structures in the atlas. To overcome this drawback, we present in this paper an alternative method to select as a template the image in a database that is the most similar to the patient to be segmented. This similarity is based on a distance between transformations. A major contribution is that we do not compute every patient-to-sample registration to find the most similar template, but only the registration of the patient towards an average image. This method has therefore the advantage of being computationally very efficient. We present a qualitative and quantitative comparison between the proposed method and a classical atlas-based segmentation method. This evaluation is performed on a subset of 45 patients using a Leave-One-Out method and shows a great improvement of the specificity of the results.