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ORBIT: a multiresolution framework for deformable registration of brain tumor images.

Authors
 Evangelia I. Zacharaki  ;  Dinggang Shen  ;  Seung-Koo Lee  ;  Christos Davatzikos 
Citation
 IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol.27(8) : 1003-1017, 2008 
Journal Title
 IEEE TRANSACTIONS ON MEDICAL IMAGING 
ISSN
 0278-0062 
Issue Date
2008
MeSH
Algorithms* ; Artificial Intelligence* ; Brain Neoplasms/diagnosis* ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods* ; Imaging, Three-Dimensional/methods* ; Pattern Recognition, Automated/methods* ; Reproducibility of Results ; Sensitivity and Specificity ; Software* ; Subtraction Technique*
Keywords
Algorithms* ; Artificial Intelligence* ; Brain Neoplasms/diagnosis* ; Humans ; Image Enhancement/methods ; Image Interpretation, Computer-Assisted/methods* ; Imaging, Three-Dimensional/methods* ; Pattern Recognition, Automated/methods* ; Reproducibility of Results ; Sensitivity and Specificity ; Software* ; Subtraction Technique*
Abstract
A deformable registration method is proposed for registering a normal brain atlas with images of brain tumor patients. The registration is facilitated by first simulating the tumor mass effect in the normal atlas in order to create an atlas image that is as similar as possible to the patient's image. An optimization framework is used to optimize the location of tumor seed as well as other parameters of the tumor growth model, based on the pattern of deformation around the tumor region. In particular, the optimization is implemented in a multiresolution and hierarchical scheme, and it is accelerated by using a principal component analysis (PCA)-based model of tumor growth and mass effect, trained on a computationally more expensive biomechanical model. Validation on simulated and real images shows that the proposed registration framework, referred to as ORBIT (optimization of tumor parameters and registration of brain images with tumors), outperforms other available registration methods particularly for the regions close to the tumor, and it has the potential to assist in constructing statistical atlases from tumor-diseased brain images.
Files in This Item:
T200801315.pdf Download
DOI
10.1109/TMI.2008.916954
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/107614
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