MARCEL (Inter-Modality Affine Registration with CorrELation Ratio): An Application for Brain Shift Correction in Ultrasound-Guided Brain Tumor Resection

  • Nima MasoumiEmail author
  • Yiming Xiao
  • Hassan Rivaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)


Tissue deformation during brain tumor removal often renders the original surgical plan invalid. This can greatly affect the quality of resection, and thus threaten the patient’s survival rate. Therefore, correction of such deformation is needed, which can be achieved through image registration between pre- and intra-operative images. We proposed a novel automatic inter-modal affine registration technique based on the correlation ratio (CR) similarity metric. The technique was demonstrated through registering intra-operative ultrasound (US) scans with magnetic resonance (MR) images of patients, who underwent brain gliomas resection. By using landmark-based mean target registration errors (TRE) for evaluation, our technique has achieved a result of 2.32 ± 0.68 mm from the initial 5.13 ± 2.78 mm.


Correlation Ratio (CR) Brain Shift Correction Mean Target Registration Error Brain Tumor Removal Image-guided Neurosurgery System (IGNS) 
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.



This work is funded by Natural Science Engineering Council of Canada (NSERC) grant RGPIN-2015-04136. The authors would like to thank anonymous reviewers for their valuable feedback.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.PERFORM CentreConcordia UniversityMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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