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LinSEM: Linearizing segmentation evaluation metrics for medical images.

Jieyu Li1, Jayaram K Udupa2, Yubing Tong2

  • 1Institute of Image Processing and Pattern Recognition, Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan RD, Shanghai 200240, China; Medical Image Processing Group, Department of Radiology, University of Pennsylvania, 602 Goddard Building, 3710 Hamilton Walk, Philadelphia, PA 19104, United States.

Medical Image Analysis
|December 8, 2019
PubMed
Summary

This study introduces LinSEM, a method to linearize medical image segmentation metrics, ensuring consistent clinical acceptability across diverse objects. LinSEM improves metric uniformity, particularly for Dice coefficient and Hausdorff Distance.

Keywords:
Acceptability scoreEvaluation metricsLinear relationshipMedical image segmentation

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Area of Science:

  • Medical Image Analysis
  • Computational Anatomy
  • Radiology

Background:

  • Current medical image segmentation metrics lack consistent clinical acceptability interpretation across objects of varying size, shape, and complexity.
  • Existing metrics do not directly reflect degrees of clinical acceptability, hindering objective evaluation.

Purpose of the Study:

  • To propose and validate LinSEM, a novel method for linearizing segmentation evaluation metrics to ensure consistent clinical acceptability.
  • To develop a metric that provides the same acceptability meaning across different anatomical structures.

Main Methods:

  • LinSEM estimates the relationship between metric values and expert-assessed acceptability scores using reader studies.
  • Simulated segmentations are generated to cover the full range of acceptability variability.
  • Metric values are linearized based on the estimated metric-acceptability relationship curve.

Main Results:

  • LinSEM significantly improves the uniformity of metric meaning across diverse objects and metrics, especially for Dice coefficient (DC) and Hausdorff Distance (HD) (8-25% improvement).
  • Jaccard index (JI) generally shows a more linear relationship with acceptability pre-linearization compared to DC and HD.
  • Linearization brings previously disparate object evaluations (e.g., right parotid gland, esophagus) into closer alignment, suggesting a body-wide evaluation approach is beneficial.

Conclusions:

  • LinSEM effectively linearizes segmentation metrics, providing a more uniform and clinically relevant evaluation of segmentation quality.
  • The method enhances the interpretability and comparability of segmentation performance across different anatomical structures and metrics.
  • Considering all objects within a region or body-wide for linearization is crucial for achieving consistent evaluation.