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Proceedings Paper

Comparison of computer-aided detection of clustered microcalcifications in digital mammography and digital breast tomosynthesis
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Paper Abstract

Digital breast tomosynthesis (DBT) has the potential to replace digital mammography (DM) for breast cancer screening. An effective computer-aided detection (CAD) system for microcalcification clusters (MCs) on DBT will facilitate the transition. In this study, we collected a data set with corresponding DBT and DM for the same breasts. DBT was acquired with IRB approval and informed consent using a GE GEN2 DBT prototype system. The DM acquired with a GE Essential system for the patient’s clinical care was collected retrospectively from patient files. DM-based CAD (CADDM) and DBT-based CAD (CADDBT) were previously developed by our group. The major differences between the CAD systems include: (a) CADDBT uses two parallel processes whereas CADDM uses a single process for enhancing MCs and removing the structured background, (b) CADDBT has additional processing steps to reduce the false positives (FPs), including ranking of candidates of cluster seeds and cluster members and the use of adaptive CNR and size thresholds at clustering and FP reduction, (c) CADDM uses convolution neural network (CNN) and linear discriminant analysis (LDA) to differentiate true microcalcifications from FPs based on their morphological and CNN features. The performance difference is assessed by FROC analysis using test set (100 views with MCs and 74 views without MCs) independent of their respective training sets. At sensitivities of 70% and 80%, CADDBT achieved FP rates of 0.78 and 1.57 per view compared to 0.66 and 2.10 per image for the CADDM. JAFROC showed no significant difference between MC detection on DM and DBT by the two CAD systems.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140K (20 March 2015); doi: 10.1117/12.2081874
Show Author Affiliations
Ravi K. Samala, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Yao Lu, Univ. of Michigan (United States)
Lubomir Hadjiiski, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Mark Helvie, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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