Share Email Print
cover

Proceedings Paper

Symmetry-based detection and diagnosis of DCIS in breast MRI
Author(s): Abhilash Srikantha; Markus T. Harz; Gillian Newstead; Lei Wang; Bram Platel; Katrin Hegenscheid; Ritse M. Mann; Horst K. Hahn; Heinz-Otto Peitgen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The delineation and diagnosis of non-mass-like lesions, most notably DCIS (ductal carcinoma in situ), is among the most challenging tasks in breast MRI reading. Even for human observers, DCIS is not always easy to diferentiate from patterns of active parenchymal enhancement or from benign alterations of breast tissue. In this light, it is no surprise that CADe/CADx approaches often completely fail to classify DCIS. Of the several approaches that have tried to devise such computer aid, none achieve performances similar to mass detection and classification in terms of sensitivity and specificity. In our contribution, we show a novel approach to combine a newly proposed metric of anatomical breast symmetry calculated on subtraction images of dynamic contrast-enhanced (DCE) breast MRI, descriptive kinetic parameters, and lesion candidate morphology to achieve performances comparable to computer-aided methods used for masses. We have based the development of the method on DCE MRI data of 18 DCIS cases with hand-annotated lesions, complemented by DCE-MRI data of nine normal cases. We propose a novel metric to quantify the symmetry of contralateral breasts and derive a strong indicator for potentially malignant changes from this metric. Also, we propose a novel metric for the orientation of a finding towards a fix point (the nipple). Our combined scheme then achieves a sensitivity of 89% with a specificity of 78%, matching CAD results for breast MRI on masses. The processing pipeline is intended to run on a CAD server, hence we designed all processing to be automated and free of per-case parameters. We expect that the detection results of our proposed non-mass aimed algorithm will complement other CAD algorithms, or ideally be joined with them in a voting scheme.

Paper Details

Date Published: 28 February 2013
PDF: 8 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701E (28 February 2013); doi: 10.1117/12.2000061
Show Author Affiliations
Abhilash Srikantha, Fraunhofer MEVIS (Germany)
Le2i, CNRS, Univ. de Bourgone (France)
Markus T. Harz, Fraunhofer MEVIS (Germany)
Gillian Newstead, The Univ. of Chicago (United States)
Lei Wang, Fraunhofer MEVIS (Germany)
Bram Platel, Fraunhofer MEVIS (Netherlands)
Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Katrin Hegenscheid, Ernst-Moritz-Arndt-Univ. Greifswald (Germany)
Ritse M. Mann, Radboud Univ. Nijmegen Medical Ctr. (Netherlands)
Horst K. Hahn, Fraunhofer MEVIS (Germany)
Heinz-Otto Peitgen, Fraunhofer MEVIS (Germany)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

© SPIE. Terms of Use
Back to Top