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Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles.

Florian Kofler1,2,3, Ivan Ezhov1,3, Lucas Fidon4

  • 1Department of Informatics, Technical University Munich, Munich, Germany.

Frontiers in Neuroscience
|January 17, 2022
PubMed
Summary
This summary is machine-generated.

We developed an unsupervised method to estimate the quality of AI segmentation, flagging unreliable results for review. This improves AI diagnostic tools for diseases like glioma and COVID-19, enhancing clinical reliability.

Keywords:
CTMROODanomaly detectionensemblingfailure predictionfusionquality estimation

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Area of Science:

  • Medical image analysis
  • Artificial intelligence in healthcare
  • Machine learning for diagnostics

Background:

  • Machine learning segmentation algorithms show promise for disease diagnosis (e.g., glioma, COVID-19) but lack reliability.
  • Failure to detect errors in AI segmentation hinders clinical adoption.
  • Existing methods struggle with uncertainty estimation in segmentation tasks.

Purpose of the Study:

  • To propose an unsupervised quality estimation method for segmentation ensembles.
  • To automatically flag error-prone segmentation results for human review.
  • To enhance the clinical translation of AI-driven diagnostic tools.

Main Methods:

  • Developed an unsupervised quality estimation technique for segmentation ensembles.
  • Analyzed discord in binary segmentation maps to identify unreliable outputs.
  • Validated the method on brain glioma and lung lesion segmentation datasets.

Main Results:

  • The method successfully flags error-prone segmentation results.
  • Demonstrated efficacy in segmenting brain glioma and lung lesions.
  • Provided an adaptive prioritization mechanism for human expert review.

Conclusions:

  • The proposed method offers reliable uncertainty estimation from segmentation ensembles.
  • Addresses the critical need for failure detection in AI segmentation.
  • Facilitates the clinical integration of AI segmentation tools by improving trust and efficiency.