DocumentCode :
3421819
Title :
Uncertainty-Driven Efficiently-Sampled Sparse Graphical Models for Concurrent Tumor Segmentation and Atlas Registration
Author :
Parisot, Sarah ; Wells, William ; Chemouny, Stephane ; Duffau, Hugues ; Paragios, Nikos
Author_Institution :
Center for Visual Comput., Ecole Centrale Paris, Paris, France
fYear :
2013
fDate :
1-8 Dec. 2013
Firstpage :
641
Lastpage :
648
Abstract :
Graph-based methods have become popular in recent years and have successfully addressed tasks like segmentation and deformable registration. Their main strength is optimality of the obtained solution while their main limitation is the lack of precision due to the grid-like representations and the discrete nature of the quantized search space. In this paper we introduce a novel approach for combined segmentation/registration of brain tumors that adapts graph and sampling resolution according to the image content. To this end we estimate the segmentation and registration marginals towards adaptive graph resolution and intelligent definition of the search space. This information is considered in a hierarchical framework where uncertainties are propagated in a natural manner. State of the art results in the joint segmentation/registration of brain images with low-grade gliomas demonstrate the potential of our approach.
Keywords :
brain; graph theory; image registration; image segmentation; medical image processing; tumours; adaptive graph resolution; atlas registration; brain tumor; concurrent tumor segmentation; deformable registration; efficiently-sampled sparse graphical model; graph-based method; grid-like representation; low-grade gliomas; quantized search space; uncertainty-driven sparse graphical model; Graphical models; Image resolution; Image segmentation; Labeling; Tumors; Uncertainty; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, VIC
ISSN :
1550-5499
Type :
conf
DOI :
10.1109/ICCV.2013.85
Filename :
6751189
Link To Document :
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