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Automatic segmentation variability estimation with segmentation priors.

L Joskowicz1, D Cohen1, N Caplan2

  • 1The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel.

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|September 13, 2018
PubMed
Summary
This summary is machine-generated.

Quantifying segmentation variability is crucial but challenging without ground truth. This study introduces a novel framework for estimating segmentation variability from a single segmentation, offering a practical solution for reliable evaluation.

Keywords:
Observer variabilitySegmentation priorsSegmentation uncertainty estimation

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Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Radiology

Background:

  • Manual and algorithmic segmentations exhibit inherent variability due to factors like image quality and observer expertise.
  • Accurate assessment of segmentation quality necessitates quantifying this variability.
  • Establishing ground truth for variability requires multiple time-consuming manual delineations.

Purpose of the Study:

  • To develop a comprehensive framework for segmentation evaluation and variability estimation without requiring ground truth.
  • To introduce a generic method for automatic segmentation variability estimation using segmentation priors and multivariate sensitivity analysis.
  • To enable reliable observer variability reference establishment and improve segmentation algorithm evaluation.

Main Methods:

  • A novel framework utilizing segmentation priors and multivariate sensitivity analysis for variability estimation.
  • Inputting an image scan and a user-validated segmentation to compute variability.
  • Combining segmentation priors with an integrator function to determine segmentation variability.

Main Results:

  • Validation studies demonstrated the method's efficacy in estimating segmentation variability.
  • Manual delineation variability for various organs and tumors was established.
  • The proposed method achieved a mean volume variability difference of <6% and a Dice similarity coefficient of >70% compared to manual delineations.

Conclusions:

  • Reliable segmentation variability estimation without ground truth is now feasible.
  • This method facilitates the establishment of observer variability references.
  • Segmentation variability estimates are essential for clinical decision-making and algorithm evaluation.