Groupwise combined segmentation and registration for atlas construction.
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.
Visual Information Processing, Department of Computing, Imperial College London.
Kanwal K
KK
Bhatia
Paul
P
Aljabar
James P
JP
Boardman
Latha
L
Srinivasan
Maria
M
Murgasova
Serena J
SJ
Counsell
Mary A
MA
Rutherford
Jo
J
Hajnal
A David
AD
Edwards
Daniel
D
Rueckert
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D002675:Child, Preschool; D003198:Computer Simulation; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007223:Infant; D007231:Infant, Newborn; D007234:Infant, Premature; D016247:Information Storage and Retrieval / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique