Computer-aided detection of microcalcifications in digital breast tomosynthesis (DBT): a multichannel signal detection approach on projection views

23 February 2012 Computer-aided detection of microcalcifications in digital breast tomosynthesis (DBT): a multichannel signal detection approach on projection views

Jun Wei, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Chuan Zhou, Yao Lu

Author Affiliations +

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 83152D (2012) https://doi.org/10.1117/12.912501
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

DBT is one of the promising imaging modalities that may improve the sensitivity and specificity for breast cancer detection. We are developing a computer-aided detection (CADe) system for clustered microcalcifications (MC) in DBT. A data set of two-view DBTs from 42 breasts was collected with a GE prototype system. We investigated a 2D approach to MC detection using projection view (PV) images rather than reconstructed 3D DBT volume. Our 2D approach consisted of two major stages: 1) detecting individual MC candidates on each PV, and 2) correlating the MC candidates from the different PVs and detecting clusters in the breast volume. With the MC candidates detected by prescreening on PVs, a trained multi-channel (MCH) filter bank was used to extract signal response from each MC candidate. A ray-tracing process was performed to fuse the MCH responses and localize the MC candidates in 3D using the geometrical information of the DBT system. Potential MC clusters were then identified by dynamic clustering of the MCs in 3D. A two-fold cross-validation method was used to train and test the CADe system. The detection performance of clustered MCs was assessed by free receiver operating characteristic (FROC) analysis. It was found that the CADe system achieved a case-based sensitivity of 90% at an average false positive rate of 2.1 clusters per DBT volume. Our study demonstrated that the CADe system using 2D MCH filter bank is promising for detection of clustered MCs in DBT.

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Jun Wei, Heang-Ping Chan, Lubomir Hadjiiski, Mark A. Helvie, Chuan Zhou, and Yao Lu "Computer-aided detection of microcalcifications in digital breast tomosynthesis (DBT): a multichannel signal detection approach on projection views", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83152D (23 February 2012); https://doi.org/10.1117/12.912501

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