A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

Ning Yu; Jia Wu; Susan P. Weinstein; Bilwaj Gaonkar; Brad M. Keller; Ahmed B. Ashraf; YunQing Jiang; Christos Davatzikos; Emily F. Conant M.D.; Despina Kontos

doi:10.1117/12.2081943

20 March 2015 A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

Ning Yu, Jia Wu, Susan P. Weinstein, Bilwaj Gaonkar, Brad M. Keller, Ahmed B. Ashraf, YunQing Jiang, Christos Davatzikos, Emily F. Conant M.D., Despina Kontos

Author Affiliations +

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94140O (2015) https://doi.org/10.1117/12.2081943
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Ning Yu, Jia Wu, Susan P. Weinstein, Bilwaj Gaonkar, Brad M. Keller, Ahmed B. Ashraf, YunQing Jiang, Christos Davatzikos, Emily F. Conant M.D., and Despina Kontos "A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94140O (20 March 2015); https://doi.org/10.1117/12.2081943

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