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Estimating Patient Specific Templates for Pre-operative and Follow-Up Brain Tumor Registration

  • Dongjin Kwon
  • Ke Zeng
  • Michel Bilello
  • Christos Davatzikos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

Abstract

Deformable registration between pre-operative and follow-up scans of glioma patients is important since it allows us to map post-operative longitudinal progression of the tumor onto baseline scans, thus, to develop predictive models of tumor infiltration and recurrence. This task is very challenging due to large deformations, missing correspondences, and inconsistent intensity profiles between the scans. Here, we propose a new method that combines registration with estimation of patient specific templates. These templates, built from pre-operative and follow-up scans along with a set of healthy brain scans, approximate the patient’s brain anatomy before tumor development. Such estimation provides additional cues for missing correspondences as well as inconsistent intensity profiles, and therefore guides better registration on pathological regions. Together with our symmetric registration framework initialized by joint segmentation-registration using a tumor growth model, we are also able to estimate large deformations between the scans effectively. We apply our method to the scans of 24 glioma patients, achieving the best performance among compared registration methods.

Keywords

Glioma Patient Registration Method Healthy Brain Nonrigid Registration Normalize Cross Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dongjin Kwon
    • 1
  • Ke Zeng
    • 1
  • Michel Bilello
    • 1
  • Christos Davatzikos
    • 1
  1. 1.Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaUSA

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