Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory

Bas Schipaanboord, Djamal Boukerroui*, Devis Peressutti, Johan van Soest, Tim Lustberg, Timor Kadir, Andre Dekker, Wouter van Elmpt, Mark Gooding

*Corresponding author for this work

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Abstract

Atlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be. This paper seeks to estimate a clinically achievable expected performance under this assumption. Assuming a perfect atlas selection, an extreme value theory has been applied to estimate the accuracy of single-atlas andmulti-atlas segmentation given a large database of atlases. For this purpose, clinical contours of most common OARs on computed tomography of the head and neck (N = 316) and thoracic (N = 280) cases were used. This paper found that while for most organs, perfect segmentation cannot be reasonably expected, auto-contouring performance of a level corresponding to clinical quality could be consistently expected given a database of 5000 atlases under the assumption of perfect atlas selection.

Original languageEnglish
Pages (from-to)99-106
Number of pages8
JournalIeee Transactions on Medical Imaging
Volume38
Issue number1
DOIs
Publication statusPublished - Jan 2019

Keywords

  • Radiotherapy
  • extreme value theory
  • atlas-based segmentation
  • auto-contouring
  • IMAGES
  • SELECTION
  • FUSION

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