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Proceedings Paper

Automatic model-based 3D segmentation of the breast in MRI
Author(s): Cristina Gallego; Anne L. Martel
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Paper Abstract

A statistical shape model (SSM) is constructed and applied to automatically segment the breast in 3D MRI. We present an approach to automatically construct a SSM: first, a population of 415 semi-automatically segmented breast MRI volumes is groupwise registered to derive an average shape. Second, a surface mesh is extracted and further decimated to reduce the density of the shape representation. Third, landmarks are obtained from the averaged decimated mesh, which are non-rigidly deformed to each individual shape in the training set, using a set of pairwise deformations. Finally, the resulting landmarks are consistently obtained in all cases of the population for further statistical shape model (SSM) generation. A leave-one-out validation demonstrated that near sub-voxel resolution reconstruction (2.5mm) error is attainable when using a minimum of 15 modes of variation. The model is further applied to automatically segment the anatomy of the breast in 3D. We illustrate the results of our segmentation approach in which the model is adjusted to the image boundaries using an iterative segmentation scheme.

Paper Details

Date Published: 11 March 2011
PDF: 8 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796215 (11 March 2011); doi: 10.1117/12.877712
Show Author Affiliations
Cristina Gallego, Univ. of Toronto (Canada)
Sunnybrook Health Sciences Ctr. (Canada)
Anne L. Martel, Univ. of Toronto (Canada)
Sunnybrook Health Sciences Ctr. (Canada)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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