Literature DB >> 16860588

Deformable registration of brain tumor images via a statistical model of tumor-induced deformation.

Ashraf Mohamed1, Evangelia I Zacharaki, Dinggang Shen, Christos Davatzikos.   

Abstract

An approach to the deformable registration of three-dimensional brain tumor images to a normal brain atlas is presented. The approach involves the integration of three components: a biomechanical model of tumor mass-effect, a statistical approach to estimate the model's parameters, and a deformable image registration method. Statistical properties of the sought deformation map from the atlas to the image of a tumor patient are first obtained through tumor mass-effect simulations on normal brain images. This map is decomposed into the sum of two components in orthogonal subspaces, one representing inter-individual differences in brain shape, and the other representing tumor-induced deformation. For a new tumor case, a partial observation of the sought deformation map is obtained via deformable image registration and is decomposed into the aforementioned spaces in order to estimate the mass-effect model parameters. Using this estimate, a simulation of tumor mass-effect is performed on the atlas image in order to generate an image that is similar to tumor patient's image, thereby facilitating the atlas registration process. Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration.

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Year:  2006        PMID: 16860588     DOI: 10.1016/j.media.2006.06.005

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  30 in total

1.  Segmentation of image ensembles via latent atlases.

Authors:  Tammy Riklin-Raviv; Koen Van Leemput; Bjoern H Menze; William M Wells; Polina Golland
Journal:  Med Image Anal       Date:  2010-06-04       Impact factor: 8.545

2.  Deformable registration of glioma images using EM algorithm and diffusion reaction modeling.

Authors:  Ali Gooya; George Biros; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2010-09-27       Impact factor: 10.048

3.  A comparative study of biomechanical simulators in deformable registration of brain tumor images.

Authors:  Evangelia I Zacharaki; Cosmina S Hogea; George Biros; Christos Davatzikos
Journal:  IEEE Trans Biomed Eng       Date:  2008-03       Impact factor: 4.538

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

Authors:  Evangelia I Zacharaki; Dinggang Shen; Seung-Koo Lee; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2008-08       Impact factor: 10.048

Review 5.  Deformable medical image registration: a survey.

Authors:  Aristeidis Sotiras; Christos Davatzikos; Nikos Paragios
Journal:  IEEE Trans Med Imaging       Date:  2013-05-31       Impact factor: 10.048

6.  Brain tumor segmentation using holistically nested neural networks in MRI images.

Authors:  Ying Zhuge; Andra V Krauze; Holly Ning; Jason Y Cheng; Barbara C Arora; Kevin Camphausen; Robert W Miller
Journal:  Med Phys       Date:  2017-08-20       Impact factor: 4.071

7.  Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.

Authors:  Yangming Ou; Hamed Akbari; Michel Bilello; Xiao Da; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2014-06-13       Impact factor: 10.048

8.  Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging.

Authors:  Xiangrui Zeng; Min Xu
Journal:  Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit       Date:  2020-08-05

9.  GLISTR: glioma image segmentation and registration.

Authors:  Ali Gooya; Kilian M Pohl; Michel Bilello; Luigi Cirillo; George Biros; Elias R Melhem; Christos Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2012-08-13       Impact factor: 10.048

10.  Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth.

Authors:  Evangelia I Zacharaki; Cosmina S Hogea; Dinggang Shen; George Biros; Christos Davatzikos
Journal:  Neuroimage       Date:  2009-07-01       Impact factor: 6.556

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