Automatic 3D lesion segmentation on breast ultrasound images

Hsien-Chi Kuo; Maryellen L. Giger; Ingrid Reiser; Karen Drukker; Alexandra Edwards; Charlene A. Sennett

doi:10.1117/12.2008014

28 February 2013 Automatic 3D lesion segmentation on breast ultrasound images

Hsien-Chi Kuo, Maryellen L. Giger, Ingrid Reiser, Karen Drukker, Alexandra Edwards, Charlene A. Sennett

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867025 (2013) https://doi.org/10.1117/12.2008014
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

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.

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Hsien-Chi Kuo, Maryellen L. Giger, Ingrid Reiser, Karen Drukker, Alexandra Edwards, and Charlene A. Sennett "Automatic 3D lesion segmentation on breast ultrasound images", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867025 (28 February 2013); https://doi.org/10.1117/12.2008014

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