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

Ravi K. Samala; Heang-Ping Chan; Yao Lu; Lubomir Hadjiiski; Jun Wei; Mark Helvie

doi:10.1117/12.2081874

20 March 2015 Comparison of computer-aided detection of clustered microcalcifications in digital mammography and digital breast tomosynthesis

Ravi K. Samala, Heang-Ping Chan, Yao Lu, Lubomir Hadjiiski, Jun Wei, Mark Helvie

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94140K (2015) https://doi.org/10.1117/12.2081874
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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 (CAD_DM) and DBT-based CAD (CAD_DBT) were previously developed by our group. The major differences between the CAD systems include: (a) CAD_DBT uses two parallel processes whereas CAD_DM uses a single process for enhancing MCs and removing the structured background, (b) CAD_DBT 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) CAD_DM 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%, CAD_DBT achieved FP rates of 0.78 and 1.57 per view compared to 0.66 and 2.10 per image for the CAD_DM. JAFROC showed no significant difference between MC detection on DM and DBT by the two CAD systems.

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Ravi K. Samala, Heang-Ping Chan, Yao Lu, Lubomir Hadjiiski, Jun Wei, and Mark Helvie "Comparison of computer-aided detection of clustered microcalcifications in digital mammography and digital breast tomosynthesis", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140K (20 March 2015); https://doi.org/10.1117/12.2081874

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