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Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and

Zahra Ghanian1, Aria Pezeshk1, Nicholas Petrick1

  • 1U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, Maryland, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|March 7, 2019
PubMed
Summary

This study shows that computationally inserted microcalcification clusters are indistinguishable from real ones, aiding in the assessment of computer-aided detection (CADe) devices for mammography. This technique reduces the need for extensive clinical data collection.

Keywords:
computer-aided detectiondigital mammographymicrocalcification insertion

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Area of Science:

  • Medical imaging
  • Artificial intelligence in healthcare
  • Radiology

Background:

  • Mammographic computer-aided detection (CADe) devices require extensive clinical data for assessment on new systems.
  • Collecting large datasets of verified mammograms is costly and time-consuming.
  • An image blending technique was developed to insert regions of interest (ROIs) from one image into another.

Purpose of the Study:

  • To evaluate the performance of an image blending technique for inserting microcalcification clusters into mammograms.
  • To determine if this technique can reduce the clinical data burden for assessing CADe devices.
  • To assess if inserted microcalcification clusters are distinguishable from native clusters by experienced observers.

Main Methods:

  • An image blending technique was used to insert microcalcification clusters from one breast into the contralateral breast of 55 patients.
  • A reader study was conducted using receiver operating characteristic (ROC) methodology to assess distinguishability.
  • A commercial CADe system was used to evaluate CADe sensitivity on mammograms with native and inserted clusters (68 cases).

Main Results:

  • Experienced observers could not reliably distinguish between inserted and native microcalcification clusters (AUC ).
  • Average by-case CADe sensitivities for native and inserted clusters were equal at 85.3%.
  • Average by-image sensitivities showed a small difference (4.7%) between native (72.3%) and inserted (67.6%) clusters.

Conclusions:

  • The image blending technique effectively creates microcalcification clusters that are indistinguishable from native ones.
  • Inserted microcalcification clusters show potential for use in assessing mammographic CADe device performance.
  • This technique may significantly reduce the need for large clinical datasets in CADe device development and validation.