Share Email Print
cover

Proceedings Paper

Detection of soft tissue densities from digital breast tomosynthesis: comparison of conventional and deep learning approaches
Author(s): Sergei V. Fotin; Yin Yin; Hrishikesh Haldankar; Jeffrey W. Hoffmeister; Senthil Periaswamy
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Computer-aided detection (CAD) has been used in screening mammography for many years and is likely to be utilized for digital breast tomosynthesis (DBT). Higher detection performance is desirable as it may have an impact on radiologist's decisions and clinical outcomes. Recently the algorithms based on deep convolutional architectures have been shown to achieve state of the art performance in object classification and detection. Similarly, we trained a deep convolutional neural network directly on patches sampled from two-dimensional mammography and reconstructed DBT volumes and compared its performance to a conventional CAD algorithm that is based on computation and classification of hand-engineered features. The detection performance was evaluated on the independent test set of 344 DBT reconstructions (GE SenoClaire 3D, iterative reconstruction algorithm) containing 328 suspicious and 115 malignant soft tissue densities including masses and architectural distortions. Detection sensitivity was measured on a region of interest (ROI) basis at the rate of five detection marks per volume. Moving from conventional to deep learning approach resulted in increase of ROI sensitivity from 0:832 ± 0:040 to 0:893 ± 0:033 for suspicious ROIs; and from 0:852 ± 0:065 to 0:930 ± 0:046 for malignant ROIs. These results indicate the high utility of deep feature learning in the analysis of DBT data and high potential of the method for broader medical image analysis tasks.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850X (24 March 2016); doi: 10.1117/12.2217045
Show Author Affiliations
Sergei V. Fotin, iCAD, Inc. (United States)
Yin Yin, iCAD, Inc. (United States)
Hrishikesh Haldankar, iCAD, Inc. (United States)
Jeffrey W. Hoffmeister, iCAD, Inc. (United States)
Senthil Periaswamy, iCAD, Inc. (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

© SPIE. Terms of Use
Back to Top