Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria
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- 作者：Hsien-Chi Kuo (1) (2)
Maryellen L. Giger (2)
Ingrid Reiser (2)
John M. Boone (3)
Karen K. Lindfors (3)
Kai Yang (3)
Alexandra Edwards (2) - 关键词：3D segmentation ; Breast ; Computer ; aided diagnosis (CAD) ; Dedicated breast CT
- 刊名：Journal of Digital Imaging
- 出版年：2014
- 出版时间：April 2014
- 年：2014
- 卷：27
- 期：2
- 页码：237-247
- 全文大小：3,910 KB
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Maryellen L. Giger (2)
Ingrid Reiser (2)
John M. Boone (3)
Karen K. Lindfors (3)
Kai Yang (3)
Alexandra Edwards (2)
1. Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
2. Department of Radiology, The University of Chicago, 5841 S. Maryland Avenue MC2026, Chicago, IL, 60637, USA
3. Department of Radiology, University of California at Davis, Sacramento, CA, 95817, USA - ISSN：1618-727X
文摘
Dedicated breast CT (bCT) produces high-resolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computer-aided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrast-enhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines. For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.