Breast segmentation in infrared thermography from characteristical inframammary shape.

Infrared thermography is gaining relevance in breast cancer assessment. For this purpose, breast segmentation in thermograms is an important task for performing automatic image analysis and detecting possible temperature changes that indicate the presence of malignancy. However, it is not a simple task since the breast limit borders, especially the top borders, often have low contrast, making it difficult to isolate the breast area. Several algorithms have been proposed for breast segmentation, but these highly depend on the contrast at the lower breast borders and on filtering algorithms to remove false edges. This work focuses on taking advantage of the distinctive inframammary shape to simplify the definition of the lower breast border, regardless of the contrast level, which indeed also provides a strong anatomical reference to support the definition of the poorly marked upper boundary of the breasts, which has been one of the major challenges in the literature. In order to demonstrate viability of the proposed technique for an automatic breast segmentation, we applied it to a database with 180 thermograms and compared their results with those reported by others in the literature. We found that our approach achieved a high performance, in terms of Intersection over Union of 0.934, even higher than that reported by artificial intelligence algorithms. The performance is invariant to breast sizes and thermal contrast of the images.

Pregnant Women with Multiple Sclerosis: An Overview of Gene Expression and Molecular Interaction Using Bioinformatics Analysis.

Multiple sclerosis (MS) is a common disease in young women of reproductive age, characterized by demyelination of the central nervous system (CNS). Understanding how genes related to MS are expressed during pregnancy can provide insights into the potential mechanisms by which pregnancy affects the course of this disease. This review article presents evidence-based studies on these patients' gene expression patterns. In addition, it constructs interaction networks using bioinformatics tools, such as STRING and KEGG pathways, to understand the molecular role of each of these genes. Bioinformatics research identified 25 genes and 21 signaling pathways, which allows us to understand pregnancy patients' genetic and biological phenomena and formulate new questions about MS during pregnancy.