Share Email Print
cover

Proceedings Paper

A fully automatic lesion detection method for DCE-MRI fat-suppressed breast images
Author(s): Anna Vignati; Valentina Giannini; Alberto Bert; Massimo Deluca; Lia Morra; Diego Persano; Laura Martincich; Daniele Regge
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction. We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results.

Paper Details

Date Published: 3 March 2009
PDF: 12 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726026 (3 March 2009); doi: 10.1117/12.811526
Show Author Affiliations
Anna Vignati, ISI Foundation (Italy)
Valentina Giannini, ISI Foundation (Italy)
Alberto Bert, im3D S.p.A. Medical Imaging Lab. (Italy)
Massimo Deluca, ISI Foundation (Italy)
Lia Morra, im3D S.p.A. Medical Imaging Lab. (Italy)
Diego Persano, im3D S.p.A. Medical Imaging Lab. (Italy)
Laura Martincich, Institute for Cancer Research and Treatment, IRCC (Italy)
Daniele Regge, Institute for Cancer Research and Treatment, IRCC (Italy)


Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

© SPIE. Terms of Use
Back to Top