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Computer-aided Detection System for Breast Masses on Digital Tomosynthesis Mammograms: Preliminary Experience

2005; Radiological Society of North America; Volume: 237; Issue: 3 Linguagem: Inglês

10.1148/radiol.2373041657

1527-1315

Heang‐Ping Chan, Jun Wei, Berkman Sahiner, Elizabeth A. Rafferty, Tao Wu, Marilyn A. Roubidoux, Richard H. Moore, Daniel B. Kopans, Lubomir M. Hadjiiski, Mark A. Helvie,

Radiomics and Machine Learning in Medical Imaging

The purpose of the study was to design a computer-aided detection (CAD) system for breast mass detection on digital breast tomosynthesis (DBT) mammograms and to perform a preliminary evaluation of the performance of this system. Twenty-six patients were imaged with a prototype DBT system. Institutional review board approval and written informed patient consent were obtained. Use of the data set in this study was HIPAA compliant. The CAD system first screened the three-dimensional volume of the mass candidates by means of gradient-field analysis. Each mass candidate was segmented from the structured background, and its image features were extracted. A feature classifier was designed to differentiate true masses from normal tissues. The CAD system was trained and tested by using a leave-one-case-out method. The classifier calculated a mean area under the test receiver operating characteristic curve of 0.91 +/- 0.03 (standard error of mean). The CAD system achieved a sensitivity of 85%, with 2.2 false-positive objects per case. The results demonstrate the feasibility of the authors' approach to the development of a CAD system for DBT mammography.