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Model for defining and reporting reference-based validation protocols in medical image processing

  • Pierre Jannin
  • Christophe Grova
  • Calvin R. MaurerJr.
Original Article

Abstract

Objectives Image processing tools are often embedded in larger systems. Validation of image processing methods is important because the performance of such methods can have an impact on the performance of the larger systems and consequently on decisions and actions based on the use of these systems. Most validation studies compare the direct or indirect results of a method with a reference that is assumed to be very close or equal to the correct solution. In this paper, we propose a model for defining and reporting reference-based validation protocols in medical image processing.

Materials and methods The model was built using an ontological approach. Its components were identified from the analysis of initial publications (mainly reviews) on medical image processing, especially registration and segmentation, and from discussions with experts from the medical imaging community during international conferences and workshops. The model was validated by its instantiation for 38 selected papers that include a validation study, mainly for medical image registration and segmentation.

Results The model includes the main components of a validation procedure and their inter-relationships. A checklist for reporting reference-based validation studies for medical image processing was also developed.

Conclusion The proposed model and associated checklist may be used in formal reference-based validation studies of registration and segmentation and for the complete and accurate reporting of such studies. The model facilitates the standardization of validation terminology and methodology, improves the comparison of validation studies and results, provides insight into the validation process, and, finally, may lead to better quality image management and decision making.

Keywords

Reference-based validation Medical image processing Image registration Segmentation Gold standard Ground truth Guidelines 

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Copyright information

© CARS 2006

Authors and Affiliations

  • Pierre Jannin
    • 1
  • Christophe Grova
    • 1
    • 2
  • Calvin R. MaurerJr.
    • 3
  1. 1.Visages, U 746, INSERM-INRIA-CNRS, Medical SchoolUniversity of RennesRennes CedexFrance
  2. 2.Montreal Neurological InstituteMcGill UniversityMontrealCanada
  3. 3.Department of NeurosurgeryStanford UniversityStanfordUSA

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