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Digital breast tomosynthesis (DBT) has emerged as a promising imaging modality for screening
mammography. However, visually detecting micro-calcification clusters depicted on DBT images is a difficult task.
Computer-aided detection (CAD) schemes for detecting micro-calcification clusters depicted on mammograms can
achieve high performance and the use of CAD results can assist radiologists in detecting subtle micro-calcification
clusters. In this study, we compared the performance of an available 2D based CAD scheme with one that includes a new
grouping and scoring method when applied to both projection and reconstructed DBT images. We selected a dataset
involving 96 DBT examinations acquired on 45 women. Each DBT image set included 11 low dose projection images
and a varying number of reconstructed image slices ranging from 18 to 87. In this dataset 20 true-positive micro-calcification
clusters were visually detected on the projection images and 40 were visually detected on the reconstructed
images, respectively. We first applied the CAD scheme that was previously developed in our laboratory to the DBT
dataset. We then tested a new grouping method that defines an independent cluster by grouping the same cluster detected
on different projection or reconstructed images. We then compared four scoring methods to assess the CAD
performance. The maximum sensitivity level observed for the different grouping and scoring methods were 70% and
88% for the projection and reconstructed images with a maximum false-positive rate of 4.0 and 15.9 per examination,
respectively. This preliminary study demonstrates that (1) among the maximum, the minimum or the average CAD
generated scores, using the maximum score of the grouped cluster regions achieved the highest performance level, (2)
the histogram based scoring method is reasonably effective in reducing false-positive detections on the projection images
but the overall CAD sensitivity is lower due to lower signal-to-noise ratio, and (3) CAD achieved higher sensitivity and higher false-positive rate (per examination) on the reconstructed images. We concluded that without changing the detection threshold or performing pre-filtering to possibly increase detection sensitivity, current CAD schemes developed and optimized for 2D mammograms perform relatively poorly and need to be re-optimized using DBT datasets and new grouping and scoring methods need to be incorporated into the schemes if these are to be used on the DBT examinations.
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