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Proceedings Paper

A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI
Author(s): Ning Yu; Jia Wu; Susan P. Weinstein; Bilwaj Gaonkar; Brad M. Keller; Ahmed B. Ashraf; YunQing Jiang; Christos Davatzikos; Emily F. Conant; Despina Kontos
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Paper Abstract

Accurate and efficient automated tumor segmentation in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is highly desirable for computer-aided tumor diagnosis. We propose a novel automatic segmentation framework which incorporates mean-shift smoothing, superpixel-wise classification, pixel-wise graph-cuts partitioning, and morphological refinement. A set of 15 breast DCE-MR images, obtained from the American College of Radiology Imaging Network (ACRIN) 6657 I-SPY trial, were manually segmented to generate tumor masks (as ground truth) and breast masks (as regions of interest). Four state-of-the-art segmentation approaches based on diverse models were also utilized for comparison. Based on five standard evaluation metrics for segmentation, the proposed framework consistently outperformed all other approaches. The performance of the proposed framework was: 1) 0.83 for Dice similarity coefficient, 2) 0.96 for pixel-wise accuracy, 3) 0.72 for VOC score, 4) 0.79 mm for mean absolute difference, and 5) 11.71 mm for maximum Hausdorff distance, which surpassed the second best method (i.e., adaptive geodesic transformation), a semi-automatic algorithm depending on precise initialization. Our results suggest promising potential applications of our segmentation framework in assisting analysis of breast carcinomas.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140O (20 March 2015); doi: 10.1117/12.2081943
Show Author Affiliations
Ning Yu, Univ. of Pennsylvania (United States)
Jia Wu, Univ. of Pennsylvania (United States)
Susan P. Weinstein, Univ. of Pennsylvania (United States)
Bilwaj Gaonkar, Univ. of Pennsylvania (United States)
Brad M. Keller, Univ. of Pennsylvania (United States)
Ahmed B. Ashraf, Univ. of Pennsylvania (United States)
YunQing Jiang, Univ. of Pennsylvania (United States)
Christos Davatzikos, Univ. of Pennsylvania (United States)
Emily F. Conant, Univ. of Pennsylvania (United States)
Despina Kontos, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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