TY - GEN
T1 - A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI
AU - Yu, Ning
AU - Wu, Jia
AU - Weinstein, Susan P.
AU - Gaonkar, Bilwaj
AU - Keller, Brad M.
AU - Ashraf, Ahmed B.
AU - Jiang, Yunqing
AU - Davatzikos, Christos
AU - Conant, Emily F.
AU - Kontos, Despina
N1 - Publisher Copyright:
© 2015 SPIE.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Breast DCE-MRI
KW - Graph-cuts
KW - Superpixel
KW - Tumor Segmentation
UR - http://www.scopus.com/inward/record.url?scp=84948767916&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84948767916&partnerID=8YFLogxK
U2 - 10.1117/12.2081943
DO - 10.1117/12.2081943
M3 - Conference contribution
AN - SCOPUS:84948767916
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2015
A2 - Hadjiiski, Lubomir M.
A2 - Hadjiiski, Lubomir M.
A2 - Tourassi, Georgia D.
A2 - Tourassi, Georgia D.
PB - SPIE
T2 - SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis
Y2 - 22 February 2015 through 25 February 2015
ER -