Reproducible Evaluation of Registration Algorithms for Movement Correction in Dynamic Contrast Enhancing Magnetic Resonance Imaging for Breast Cancer Diagnosis

  • I. A. IllanEmail author
  • J. Ramirez
  • J. M. Gorriz
  • K. Pinker
  • A. Meyer-Baese
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


Accurate methods for computer aided diagnosis of breast cancer increase accuracy of detection and provide support to physicians in detecting challenging cases. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), motion artifacts can appear as a result of patient displacements. Non-linear deformation algorithms for breast image registration provide with a solution to the correspondence problem in contrast with affine models. In this study we evaluate 3 popular non-linear registration algorithms: MIRTK, Demons, SyN Ants, and compare to the affine baseline. We propose automatic measures for reproducible evaluation on the DCE-MRI breast-diagnosis TCIA-database, based on edge detection and clustering algorithms, and provide a rank of the methods according to these measures.


Medical image processing Reproducibility DCE-MRI Registration Diffeomorphism Optical flow Non-affine registration 



This work has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 656886.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • I. A. Illan
    • 1
    • 2
    Email author
  • J. Ramirez
    • 2
  • J. M. Gorriz
    • 2
  • K. Pinker
    • 3
    • 4
  • A. Meyer-Baese
    • 1
  1. 1.Scientific Computing DepartmentFlorida State UniversityTallahasseeUSA
  2. 2.Department of Signal Theory, Networking and Communications, DaSCI (Data Science and Computational Intelligence Research Institute)Universidad de GranadaGranadaSpain
  3. 3.Department of RadiologyMemorial Sloan-Kettering Cancer CenterNew YorkUSA
  4. 4.Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender ImagingMedical University of Vienna/AKH WienViennaAustria

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