Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications

Authors

  • Mordang Jan-Jurre,

    1. Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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  • Gubern-Mérida Albert,

    1. Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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  • den Heeten Gerard,

    1. The National Training Centre for Breast Cancer Screening, Nijmegen 6503 GJ, The Netherlands and Department of Radiology, Amsterdam Medical Center, Amsterdam 1100 DD, The Netherlands
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  • Karssemeijer Nico

    1. Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
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Abstract

Purpose:

In the past decades, computer-aided detection (CADe) systems have been developed to aid screening radiologists in the detection of malignant microcalcifications. These systems are useful to avoid perceptual oversights and can increase the radiologists’ detection rate. However, due to the high number of false positives marked by these CADe systems, they are not yet suitable as an independent reader. Breast arterial calcifications (BACs) are one of the most frequent false positives marked by CADe systems. In this study, a method is proposed for the elimination of BACs as positive findings. Removal of these false positives will increase the performance of the CADe system in finding malignant microcalcifications.

Methods:

A multistage method is proposed for the removal of BAC findings. The first stage consists of a microcalcification candidate selection, segmentation and grouping of the microcalcifications, and classification to remove obvious false positives. In the second stage, a case-based selection is applied where cases are selected which contain BACs. In the final stage, BACs are removed from the selected cases. The BACs removal stage consists of a GentleBoost classifier trained on microcalcification features describing their shape, topology, and texture. Additionally, novel features are introduced to discriminate BACs from other positive findings.

Results:

The CADe system was evaluated with and without BACs removal. Here, both systems were applied on a validation set containing 1088 cases of which 95 cases contained malignant microcalcifications. After bootstrapping, free-response receiver operating characteristics and receiver operating characteristics analyses were carried out. Performance between the two systems was compared at 0.98 and 0.95 specificity. At a specificity of 0.98, the sensitivity increased from 37% to 52% and the sensitivity increased from 62% up to 76% at a specificity of 0.95. Partial areas under the curve in the specificity range of 0.8–1.0 were significantly different between the system without BACs removal and the system with BACs removal, 0.129 ± 0.009 versus 0.144 ± 0.008 (p<0.05), respectively. Additionally, the sensitivity at one false positive per 50 cases and one false positive per 25 cases increased as well, 37% versus 51% (p<0.05) and 58% versus 67% (p<0.05) sensitivity, respectively. Additionally, the CADe system with BACs removal reduces the number of false positives per case by 29% on average. The same sensitivity at one false positive per 50 cases in the CADe system without BACs removal can be achieved at one false positive per 80 cases in the CADe system with BACs removal.

Conclusions:

By using dedicated algorithms to detect and remove breast arterial calcifications, the performance of CADe systems can be improved, in particular, at false positive rates representative for operating points used in screening.

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