Average brain models: a convergence study
Computer Vision and Image Understanding - Special issue on analysis of volumetric image
A Digital Brain Atlas for Surgical Planning, Model-Driven Segmentation, and Teaching
IEEE Transactions on Visualization and Computer Graphics
Segmentation of brain MRI in young children
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A unified information-theoretic approach to groupwise non-rigid registration and model building
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Joint Segmentation of Image Ensembles via Latent Atlases
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Segmentation of brain images using adaptive atlases with application to ventriculomegaly
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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The creation of average anatomical atlases has been a growing area of research in recent years. It is of increased value to construct representations of, not only intensity atlases, but also their segmentation into required tissues or structures. This paper presents novel groupwise combined segmentation and registration approaches, which aim to simultaneously improve both the alignment of intensity images to their average shape, as well as the segmentations of structures in the average space. An iterative EM framework is used to build average 3D MR atlases of populations for which prior atlases do not currently exist: preterm infants at one- and two-years old. These have been used to quantify the growth of tissues occurring between these ages.