Active shape models—their training and application
Computer Vision and Image Understanding
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Identifying nuclear phenotypes using semi-supervised metric learning
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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We present a study of the spatial variation of nuclear morphology of stromal and cancer-associated fibroblasts in the mouse mammary gland. The work is part of a framework being developed for the analysis of the tumor microenvironment in breast cancer. Recent research has uncovered the role of stromal cells in promoting tumor growth and progression. In specific, studies have indicated that stromal fibroblasts - formerly considered to be passive entities in the extracellular matrix - play an active role in the progression of tumor in mammary tissue. We have focused on the analysis of the nuclear morphology of fibroblasts, which several studies have shown to be a critical phenotype in cancer. An essential component of our approach is that the nuclear morphology is studied within the 3D spatial context of the tissue, thus enabling us to pose questions about how the locus of a cell relates to its morphology, and possibly to its function. In order to make quantitative comparisons between nuclear populations, we build statistical shape models of cell populations and infer differences between the populations through these models. We present our observation on both normal and tumor tissues from the mouse mammary gland.