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Relationship between computer segmentation performance and computer classification performance in breast CT: A

Juhun Lee1, Robert M Nishikawa1, Ingrid Reiser2

  • 1Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.

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|June 20, 2018
PubMed
Summary
This summary is machine-generated.

Better breast cancer computer-aided diagnosis (CADx) segmentation does not always improve classification accuracy. Researchers found that while moderate correlations exist, segmentation performance doesn't guarantee classification outcomes, highlighting the need to report both metrics.

Keywords:
breast CTcomputer classificationcomputer segmentationcomputer-aided diagnosis

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Area of Science:

  • Medical imaging analysis
  • Computer-aided diagnosis (CADx)
  • Breast cancer detection

Background:

  • Computer-aided diagnosis (CADx) tools for breast cancer often rely on lesion segmentation and classification.
  • The assumption that improved segmentation directly leads to better classification has not been rigorously evaluated.

Purpose of the Study:

  • To evaluate the relationship between computer segmentation performance and computer classification performance in breast cancer detection.
  • To determine if enhanced segmentation accuracy consistently improves malignancy classification outcomes.

Main Methods:

  • Analysis of 85 breast lesions (32 benign, 56 malignant) from breast CT scans of 82 women.
  • Simulated 15 segmentation algorithms and generated 15 classification outcomes using quantitative image features.
  • Evaluated segmentation and classification performance relationships using 10-fold cross-validation across smooth and sharp iterative image reconstructions (IIR) and clinical reconstructions.

Main Results:

  • A low positive correlation (median rho = 0.18) was observed between segmentation and classification for smooth IIR.
  • Moderate positive correlations (median rho = 0.4-0.43) were found for sharp IIR and clinical reconstructions.
  • Significant variations in both segmentation and classification performances were noted, with similar segmentation not always yielding similar classification results.

Conclusions:

  • Computer segmentation is an indirect factor influencing computer classification accuracy in breast cancer CADx.
  • Improved segmentation performance does not guarantee improved classification performance.
  • Both segmentation and classification metrics should be reported when comparing segmentation algorithms.