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Proceedings Paper

Digital breast tomosynthesis: feasibility of automated detection of microcalcification clusters on projections views
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Paper Abstract

We are developing a computer-aided detection (CAD) system to assist radiologists in detecting microcalcification clusters in digital breast tomosynthesis (DBT). The purpose of this study is to investigate the feasibility of a 2D approach using the projection-view (PV) images as input. In the first stage, automated detection of the microcalcification clusters on the PVs is performed. In the second stage, the detected cluster candidates or the individual microcalcifications on the PVs are back-projected to the 3D volume. The true clusters or microcalcifications will therefore converge at their focal planes and ideally will result in higher cluster or microcalcification scores than the FPs. In the final step an analysis of the back-projected cluster or microcalcification candidates is performed to differentiate the true and false clusters. In this pilot study, a limited data set of 39 cases with biopsy proven microcalcification clusters (17 malignant, 22 benign) was used. The DBT scans were obtained in both CC and MLO views using a GE GEN2 prototype system which acquires 21 PVs over a 60º arc in 3º increments. In the 78 DBT volumes, a total of 74 clusters (33 malignant clusters in 34 breasts and 41 benign clusters in 44 breasts) were identified by an experienced radiologist. The computer detected 61% (956/1554) of the clusters on the PVs from the 74 scans. After back-projection of the microcalcification candidates detected on the individual PVs and excluding the first few PVs that had higher noise in back-projection stage, 84% (62/74) of the true clusters were detected in the 3D volume. Study is underway to develop methods to reduce FPs and to compare this 2D approach with 3D or combined 2D and 3D approaches.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241F (9 March 2010); doi: 10.1117/12.844627
Show Author Affiliations
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Mark A. Helvie, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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