Paper Info
Title
Precise anatomy localization in CT data by an improved probabilistic tissue type atlas.
Abstract
Automated interpretation of CT scans is an important, clinically relevant area as the number of such scans is increasing rapidly and the interpretation is time consuming. Anatomy localization is an important prerequisite for any such interpretation task. This can be done by image-to-atlas registration, where the atlas serves as a reference space for annotations such as organ probability maps. Tissue type based atlases allow fast and robust processing of arbitrary CT scans. Here we present two methods which significantly improve organ localization based on tissue types. A first problem is the definition of tissue types, which until now is done heuristically based on experience. We present a method to determine suitable tissue types from sample images automatically. A second problem is the restriction of the transformation space: all prior approaches use global affine maps. We present a hierarchical strategy to refine this global affine map. For each organ or region of interest a localized tissue type atlas is computed and used for a subsequent local affine registration step. A three-fold cross validation on 311 CT images with different fields-of-view demonstrates a reduction of the organ localization error by 33%.
Year
DOI
Venue
2016
10.1117/12.2209036
Proceedings of SPIE
Keywords
Field
DocType
CT images,probabilistic atlas,tissue types,global and local registration,field-of-view detection,organ localization
Affine transformation,Computer vision,Heuristic,Anatomy,Probabilistic atlas,Atlas (anatomy),Artificial intelligence,Region of interest,Probabilistic logic,Cross-validation,Physics
Conference
Volume
ISSN
Citations 
9784
0277-786X
0
PageRank 
References 
Authors
0.34
1
6
Name
Order
Citations
PageRank
Astrid Franz114011.98
nicole schadewaldt201.35
Heinrich Schulz393.69
Vik, T.432.17
Martin Bergtholdt573.71
Daniel Bystrov6628.62