Breast carcinomas are cancers that arise from the epithelial cells of the breast, which are the cells that line the lobules and the lactiferous ducts. Breast carcinoma is the most common type of breast cancer and can be divided into different subtypes based on architectural features and growth patterns, recognized during a histopathological examination. Tumor microenvironment (TME) is the cellular environment in which tumor cells develop. Being composed of various cell types having different biological roles, TME is recognized as playing an important role in the progression of the disease. The architectural heterogeneity in breast carcinomas and the spatial interactions with TME are, to date, not well understood. Developing a spatial model of tumor architecture and spatial interactions with TME can advance our understanding of tumor heterogeneity. Furthermore, generating histological synthetic datasets can contribute to validating, and comparing analytical methods that are used in digital pathology. In this work, we propose a modeling method that applies to different breast carcinoma subtypes and TME spatial distributions based on mathematical morphology. The model is based on a few morphological parameters that give access to a large spectrum of breast tumor architectures and are able to differentiate in-situ ductal carcinomas (DCIS) and histological subtypes of invasive carcinomas such as ductal (IDC) and lobular carcinoma (ILC). In addition, a part of the parameters of the model controls the spatial distribution of TME relative to the tumor. The validation of the model has been performed by comparing morphological features between real and simulated images.
The morphology of intestinal glands is an important and significant indicator of the level of the severity of an inflammatory bowel disease, and has also been used routinely by pathologists to evaluate the malignancy and the prognosis of colorectal cancers such as adenocarcinomas. The extraction of meaningful information describing the morphology of glands relies on an accurate segmentation method. In this work, we propose a novel technique based on mathematical morphology that characterizes the spatial positioning of nuclei for intestinal gland segmentation in histopathological images. According to their appearance, glands can be divided into two types: hallow glands and solid glands. Hallow glands are composed of lumen and/or goblet cells cytoplasm, or filled with abscess in some advanced stages of the disease, while solid glands are composed of bunches of cells clustered together and can also be filled with necrotic debris. Given this scheme, an efficient characterization of the spatial distribution of cells is sufficient to carry out the segmentation. In this approach, hallow glands are first identified as regions empty of nuclei and surrounded by thick layers of epithelial cells, then solid glands are identified by detecting regions crowded of nuclei. First, cell nuclei are identified by color classification. Then, morphological maps are generated by the mean of advanced morphological operators applied to nuclei objects in order to interpret their spatial distribution and properties to identify candidates for glands central-regions and epithelial layers that are combined to extract the glandular structures.
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