Joint Myocardial Registration and Segmentation of Cardiac BOLD MRI

  • Ilkay Oksuz
  • Rohan Dharmakumar
  • Sotirios A. Tsaftaris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


Registration and segmentation of anatomical structures are two well studied problems in medical imaging. Optimizing segmentation and registration jointly has been proven to improve results for both challenges. In this work, we propose a joint optimization scheme for registration and segmentation using dictionary learning based descriptors. Our joint registration and segmentation aims to solve an optimization function, which enables better performance for both of these ill-posed processes. We build two dictionaries for background and myocardium for square patches extracted from training images. Based on dictionary learning residuals and sparse representations defined on these pre-trained dictionaries, a Markov Random Field (MRF) based joint optimization scheme is built. The algorithm proceeds iteratively updating the dictionaries in an online fashion. The accuracy of the proposed method is illustrated on Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP-BOLD) MRI and standard cine Cardiac MRI data from MICCAI 2013 SATA Segmentation Challenge. The proposed joint segmentation and registration method achieves higher dice accuracy for myocardium segmentation compared to its variants.


Segmentation Registration Markov Random Fields Joint optimization BOLD CINE MR 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ilkay Oksuz
    • 1
    • 2
  • Rohan Dharmakumar
    • 3
  • Sotirios A. Tsaftaris
    • 2
  1. 1.IMT Institute for Advanced Studies LuccaLuccaItaly
  2. 2.The University of EdinburghEdinburghUK
  3. 3.Biomedical Imaging Research InstituteCedars-Sinai MedicalLos AngelesUSA

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