Artificially enlarged training set in image segmentation
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Breast shapes on real and simulated mammograms
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
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This paper describes the construction of 3D statistical deformation models (SDMs) from biomechanical simulations. The method was used to capture the average breast motion and its variability due to compressing it between two plates as performed for X-ray mammography. One SDM described the motion from the compressed to the undeformed state. Another SDM captured the deformation difference due to variations in patient positioning and compression magnitude. Such models could prove useful for guiding the development of algorithms to register serial X-ray mammograms. The SDMs are based on simulating plausible breast compressions for a population of 20 patients via finite element models created from segmented 3D MR breast images. Tissue properties and boundary conditions were varied according to reported values. Three compression configurations (called current, prior1, prior2) were simulated per breast. The associated displacement fields were mapped into a common space and SDMs were generated using principle component analysis. Leave-one-patient-out tests showed that these models can reduce the mean error of unseen deformations on average by 87% (19.35 mm to 2.49 mm for current-to-undeformed, 13.49 mm to 1.70 mm for current-to-prior) when using the first 16 modes of variation.