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9 March 2010 Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images
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Recently, tomosynthesis (DBT) and CT (BCT) have been developed for breast imaging. Since each modality produces a fundamentally different representation of the breast volume, our goal was to investigate whether a 3D segmentation algorithm for breast masses could be applied to both DBT and breast BCT images. A secondary goal of this study was to investigate a simplified method for comparing manual outlines to a computer segmentation. The seeded mass lesion segmentation algorithm is based on maximizing the radial gradient index (RGI) along a constrained region contour. In DBT, the constraint function was a prolate spherical Gaussian, with a larger FWHM along the depth direction where the resolution is low, while it was a spherical Gaussian for BCT. For DBT, manual lesion outlines were obtained in the in-focus plane of the lesion, which was used to compute the overlap ratio with the computer segmentation. For BCT, lesions were manually outlined in three orthogonal planes, and the average overlap ratio from the three planes was computed. In DBT, 81% of all lesions were segmented at an overlap ratio of 0.4 or higher, based on manual outlines in one slice through the lesion center. In BCT, 93% of all segmentations achieved an average overlap ratio of 0.4, based on the manual outlines in three orthogonal planes. Our results indicate mass lesions in both BCT and DBT images can be segmented with the proposed 3D segmentation algorithm, by selecting an appropriate set of parameters and after images have undergone specific pre-processing.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
I. Reiser, S. P. Joseph, R. M. Nishikawa, M. L. Giger, J. Boone, K. Lindfors, A. Edwards, N. Packard, R. H. Moore, and D. B. Kopans "Evaluation of a 3D lesion segmentation algorithm on DBT and breast CT images", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242N (9 March 2010);

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