Characterization of material properties of human skin is required to develop a physics-based biomechanical model that can predict deformation of female breast after cosmetic and reconstructive surgery. In this paper, we have adopted an experimental approach to characterize the biaxial response of human skin using bulge tests. Skin specimens were harvested from breast and abdominal skin of female subjects who underwent mastectomy and/or reconstruction at The University of Texas MD Anderson Cancer Center and who provided informed consent. The specimens were tested within 2 h of harvest, and after freezing for different time periods but not exceeding 6 months. Our experimental results show that storage in a freezer at −20 °C for up to about 40 days does not lead to changes in the mechanical response of the skin beyond statistical variation. Moreover, displacement at the apex of the bulged specimen versus applied pressure varies significantly between different specimens from the same subject and from different subjects. The bulge test results were used in an inverse optimization procedure in order to calibrate two different constitutive material models-the angular integration model proposed by Lanir (1983) and the generalized structure tensor formulation of Gasser et al. (2006). The material parameters were estimated through a cost function that penalized deviations of the displacement and principal curvatures at the apex. Generally, acceptable fits were obtained with both models, although the angular integration model was able to fit the curvatures slightly better than the Gasser et al. model. The range of the model parameters has been extracted for use in physics-based biomechanical models of the breast.

Pectoral muscle segmentation is a crucial step in various computeraided applications of breast Magnetic Resonance Imaging (MRI). Due to imaging artifact and homogeneity between the pectoral and breast regions, the pectoral muscle boundary estimation is not a trivial task. In this paper, a fully automatic segmentation method based on deep learning is proposed for accurate delineation of the pectoral muscle boundary in axial breast MR images. The proposed method involves two main steps: pectoral muscle segmentation and boundary estimation. For pectoral muscle segmentation, a model based on the U-Net architecture is used to segment the pectoral muscle from the input image. Next, the pectoral muscle boundary is estimated through candidate points detection and contour segmentation. The proposed method was evaluated quantitatively with two realworld datasets, our own private dataset, and a publicly available dataset. The first dataset includes 12 patients breast MR images and the second dataset consists of 80 patients breast MR images. The proposed method achieved a Dice score of 95% in the first dataset and 89% in the second dataset. The high segmentation performance of the proposed method when evaluated on large scale quantitative breast MR images confirms its potential applicability in future breast cancer clinical applications.