NDLI: Atlas image labeling of subcortical structures and vascular territories in brain CT images

Content Provider	IEEE Xplore Digital Library
Author	Kaifang Du Li Zhang Nguyen, T. Ordy, V. Fichte, H. Ditt, H. Chefd'hotel, C.
Copyright Year	2013
Description	Author affiliation: Dept. of Biomed. Eng., Univ. of Iowa, Iowa City, IA, USA (Kaifang Du) \|\| Imaging & Therapy Div., Siemens AG, Forchheim, Germany (Fichte, H.; Ditt, H.) \|\| Imaging & Comput. Vision, Siemens Corp. Technol., Princeton, NJ, USA (Li Zhang; Nguyen, T.; Ordy, V.; Chefd'hotel, C.)
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.
Starting Page	6530
Ending Page	6534
File Size	443946
Page Count	5
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6611051
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Labeling Computed tomography Accuracy Image segmentation High definition video Arteries
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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