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Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation

  • Ipek Oguz
  • Aaron Carass
  • Dzung L. Pham
  • Snehashis Roy
  • Nagesh Subbana
  • Peter A. Calabresi
  • Paul A. Yushkevich
  • Russell T. Shinohara
  • Jerry L. Prince
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10670)

Abstract

The Dice overlap ratio is commonly used to evaluate the performance of image segmentation algorithms. While Dice overlap is very useful as a standardized quantitative measure of segmentation accuracy in many applications, it offers a very limited picture of segmentation quality in complex segmentation tasks where the number of target objects is not known a priori, such as the segmentation of white matter lesions or lung nodules. While Dice overlap can still be used in these applications, segmentation algorithms may perform quite differently in ways not reflected by differences in their Dice score. Here we propose a new set of evaluation techniques that offer new insights into the behavior of segmentation algorithms. We illustrate these techniques with a case study comparing two popular multiple sclerosis (MS) lesion segmentation algorithms: OASIS and LesionTOADS.

Keywords

Segmentation Evaluation MS Lesion 

Notes

Acknowledgments

This work was supported, in part, by NIH grants NINDS R01-NS094456, NINDS R01-NS085211, NINDS R21-NS093349, NIBIB R01-EB017255, NINDS R01-NS082347, NINDS R01-NS070906, as well as National MS Society grant RG-1507-05243.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Ipek Oguz
    • 1
  • Aaron Carass
    • 2
    • 3
  • Dzung L. Pham
    • 4
  • Snehashis Roy
    • 4
  • Nagesh Subbana
    • 1
  • Peter A. Calabresi
    • 5
  • Paul A. Yushkevich
    • 1
  • Russell T. Shinohara
    • 6
  • Jerry L. Prince
    • 2
    • 3
  1. 1.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of Electrical and Computer EngineeringThe Johns Hopkins UniversityBaltimoreUSA
  3. 3.Department of Computer ScienceThe Johns Hopkins UniversityBaltimoreUSA
  4. 4.CNRMThe Henry M. Jackson Foundation for the Advancement of Military MedicineBethesdaUSA
  5. 5.Department of NeurologyThe Johns Hopkins University School of MedicineBaltimoreUSA
  6. 6.Department of Biostatistics and EpidemiologyUniversity of PennsylvaniaPhiladelphiaUSA

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