Paper Info
Title
Atlas image labeling of subcortical structures and vascular territories in brain CT images.
Abstract
We propose a multi-atlas labeling method for subcortical structures and cerebral vascular territories in brain CT images. Each atlas image is registered to the query image by a non-rigid registration and the deformation is then applied to the labeling of the atlas image to obtain the labeling of the query image. Four label fusion strategies (single atlas, most similar atlas, major voting, and STAPLE) were compared. Image similarity values in non-rigid registration were calculated and used to select and rank atlases. Major voting fusion strategy gave the best accuracy, with DSC (Dice similarity coefficient) around 0.85 ± 0.03 for caudate, putamen, and thalamus. The experimental results also show that fusing more atlases does not necessarily yield higher accuracy and we should be able to improve accuracy and decrease computation cost at the same time by selecting a preferred set with the minimum number of atlases.
Year
DOI
Venue
2013
10.1109/EMBC.2013.6611051
EMBC
Keywords
Field
DocType
computerised tomography,dice similarity coefficient,image fusion,atlas image labeling,computed tomography,thalamus,dsc,label fusion strategy,nonrigid registration,subcortical structures,deformation,cerebral vascular territory,atlas image registration,brain,brain ct images,multiatlas labeling method,image registration,query image labeling,putamen,medical image processing,caudate,image segmentation,accuracy,labeling
Brain mapping,Computer vision,Image fusion,Computer science,Atlas (anatomy),Artificial intelligence,Neuroimaging,Image registration,Image labeling
Conference
Volume
ISSN
Citations 
2013
1557-170X
0
PageRank 
References 
Authors
0.34
3
7
Name
Order
Citations
PageRank
Kaifang Du100.34
Li Zhang2235.03
Tony Nguyen300.34
Vincent Ordy400.34
Heinz Fichte500.34
Hendrik Ditt600.68
Christophe Chefd'hotel700.34