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Proceedings Paper

Automatic 3D lesion segmentation on breast ultrasound images
Author(s): Hsien-Chi Kuo; Maryellen L. Giger; Ingrid Reiser; Karen Drukker; Alexandra Edwards; Charlene A. Sennett
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Paper Abstract

Automatically acquired and reconstructed 3D breast ultrasound images allow radiologists to detect and evaluate breast lesions in 3D. However, assessing potential cancers in 3D ultrasound can be difficult and time consuming. In this study, we evaluate a 3D lesion segmentation method, which we had previously developed for breast CT, and investigate its robustness on lesions on 3D breast ultrasound images. Our dataset includes 98 3D breast ultrasound images obtained on an ABUS system from 55 patients containing 64 cancers. Cancers depicted on 54 US images had been clinically interpreted as negative on screening mammography and 44 had been clinically visible on mammography. All were from women with breast density BI-RADS 3 or 4. Tumor centers and margins were indicated and outlined by radiologists. Initial RGI-eroded contours were automatically calculated and served as input to the active contour segmentation algorithm yielding the final lesion contour. Tumor segmentation was evaluated by determining the overlap ratio (OR) between computer-determined and manually-drawn outlines. Resulting average overlap ratios on coronal, transverse, and sagittal views were 0.60 ± 0.17, 0.57 ± 0.18, and 0.58 ± 0.17, respectively. All OR values were significantly higher the 0.4, which is deemed “acceptable”. Within the groups of mammogram-negative and mammogram-positive cancers, the overlap ratios were 0.63 ± 0.17 and 0.56 ± 0.16, respectively, on the coronal views; with similar results on the other views. The segmentation performance was not found to be correlated to tumor size. Results indicate robustness of the 3D lesion segmentation technique in multi-modality 3D breast imaging.

Paper Details

Date Published: 28 February 2013
PDF: 6 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867025 (28 February 2013); doi: 10.1117/12.2008014
Show Author Affiliations
Hsien-Chi Kuo, Univ. of Illinois at Chicago (United States)
The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Ingrid Reiser, The Univ. of Chicago (United States)
Karen Drukker, The Univ. of Chicago (United States)
Alexandra Edwards, The Univ. of Chicago (United States)
Charlene A. Sennett, The Univ. of Chicago (United States)

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

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