Groupwise combined segmentation and registration for atlas construction

K. K. Bhatia, P. Aljabar, J. P. Boardman, L. Srinivasan, M. Murgasova, S. J. Counsell, M. A. Rutherford, J. Hajnal, A. D. Edwards, D. Rueckert

Research output: Chapter in Book/Report/Conference proceedingConference paper

35 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention - MICCAI 2007
Subtitle of host publication10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part1
EditorsNicholas Ayache, Sebastien Ourselin, Anthony Maeder
Place of PublicationBerlin ; New York
PublisherSpringer Berlin Heidelberg
Number of pages9
ISBN (Print)9783540757566
Publication statusPublished - 2007

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743


  • Algorithms *Artificial Intelligence Brain/*anatomy & histology Child, Preschool Computer Simulation Databases, Factual Female Humans Image Enhancement/methods Image Interpretation, Computer-Assisted/*methods Imaging, Three-Dimensional/methods Infant Infant, Newborn Infant, Premature Information Storage and Retrieval/methods Magnetic Resonance Imaging/*methods Male *Models, Anatomic Pattern Recognition, Automated/*methods Reproducibility of Results Sensitivity and Specificity *Subtraction Technique


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