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Proceedings Paper

Breast mass segmentation on dynamic contrast-enhanced magnetic resonance scans using the level set method
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Paper Abstract

The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced (DCE) magnetic resonance (MR) scans that were performed to monitor breast cancer response to neoadjuvant chemotherapy. A radiologist experienced in interpreting breast MR scans defined the mass using a cuboid volume of interest (VOI). Our method then used the K-means clustering algorithm followed by morphological operations for initial mass segmentation on the VOI. The initial segmentation was then refined by a three-dimensional level set (LS) method. The velocity field of the LS method was formulated in terms of the mean curvature which guaranteed the smoothness of the surface and the Sobel edge information which attracted the zero LS to the desired mass margin. We also designed a method to reduce segmentation leak by adapting a region growing technique. Our method was evaluated on twenty DCE-MR scans of ten patients who underwent neoadjuvant chemotherapy. Each patient had pre- and post-chemotherapy DCE-MR scans on a 1.5 Tesla magnet. Computer segmentation was applied to coronal T1-weighted images. The in-plane pixel size ranged from 0.546 to 0.703 mm and the slice thickness ranged from 2.5 to 4.0 mm. The flip angle was 15 degrees, repetition time ranged from 5.98 to 6.7 ms, and echo time ranged from 1.2 to 1.3 ms. The computer segmentation results were compared to the radiologist's manual segmentation in terms of the overlap measure defined as the ratio of the intersection of the computer and the radiologist's segmentations to the radiologist's segmentation. Pre- and post-chemotherapy masses had overlap measures of 0.81±0.11 (mean±s.d.) and 0.70±0.21, respectively.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152A (17 March 2008); doi: 10.1117/12.771390
Show Author Affiliations
Jiazheng Shi, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Chintana Paramagul, Univ. of Michigan (United States)
Lubomir M. Hadjiiski, Univ. of Michigan (United States)
Mark Helvie, Univ. of Michigan (United States)
Yi-Ta Wu, Univ. of Michigan (United States)
Jun Ge, Univ. of Michigan (United States)
Yiheng Zhang, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)
Jun Wei, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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