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Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a

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Summary

This study compared four methods for quantifying uncertainty in AI-driven medical image segmentation. Test-time augmentation (TTA) offered the most reliable uncertainty estimates for renal cancer CT scans.

Keywords:
Bayesian convolutional neural networkMonte Carlo dropout and dropconnectmulti‐class segmentationrisk propensity degreeuncertainty quantification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Convolutional Neural Networks (CNNs) automate medical image segmentation, reducing time and variability.
  • Clinical adoption is hindered by challenges in accurately quantifying segmentation uncertainty.

Purpose of the Study:

  • Evaluate uncertainty quantification from two Bayesian and two non-Bayesian CNN approaches.
  • Compare risk propensity of these methods for renal cancer (RC) CT image segmentation.

Main Methods:

  • Implemented Bayesian CNNs (BDR, BDC), ensemble (Ens), and test-time augmentation (TTA) methods.
  • Compared segmentation accuracy (Dice score) and uncertainty (RCC, RIU) on Kits21 and other renal CT datasets.
  • Assessed performance against inter-observer variability.

Main Results:

  • Accuracy varied by structure (Dice: kidney 0.92, tumor 0.58, cyst 0.21).
  • TTA showed highest uncertainty; BDR showed lowest, but with more incorrect-certain pixels.
  • BDR was risk-taking (higher accuracy, less uncertainty on errors), while others were conservative (more false alarms).
  • TTA demonstrated the highest agreement with inter-observer variability (Dice = 0.94).

Conclusions:

  • Quantifying segmentation uncertainty is crucial for clinical decision-making.
  • The choice of method should align with the application's required risk propensity and policy.