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    Automated segmentation of endometriosis using transfer learning technique
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    Abstract:
    Background: This paper focuses on segmenting the exact location of endometriosis using the state-of-art technique known as U-Net. Endometriosis is a progressive disorder that has a significant impact on women. The lesion-like appearance that grows inside the uterus and sheds for every periodical cycle is known as endometriosis. If the lesion exists and is transferred to other locations in the women’s reproductive system, it may lead to a serious problem. Besides radiologists deep learning techniques exist for recognizing the presence and aggravation of endometriosis. Methods: The proposed method known as structural similarity analysis of endometriosis (SSAE) identifies the similarity between pathologically identified and annotated images obtained from standardized dataset known as GLENDA v1.5 by implementing two systematic approaches. The first approach is based on semantic segmentation and the second approach uses statistical analysis. Semantic segmentation is a cutting-edge technology for identifying exact locations by performing pixel-level classification. In semantic segmentation, U-Net is a transfer-learning architecture that works effectively for biomedical image classification. The SSAE implements the U-Net architecture for segmenting endometriosis based on the region of occurrence. The second approach proves the similarity between pathologically identified images and the corresponding annotated images using a statistical evaluation. Statistical analysis was performed using calculation of both the mean and standard deviation of all four regions by implementing systematic sampling procedure. Results: The SSAE obtains the intersection over union value of 0.72 and the F1 score of 0.74 for the trained dataset. The means of both the laparoscopic and annotated images for all regions were similar. Consequently, the SSAE facilitated the presence of abnormalities in a specific region. Conclusions: The proposed SSAE approach identifies the affected region using U-Net architecture and systematic sampling procedure.
    This chapter contains sections titled: Epidemiology Etiology and pathogenesis Key genes and pathways associated with the development and propagation of endometriosis Modeling endometriosis: animal models of disease Link between endometriosis and cancer Current surgical and medical therapeutic strategies for endometriosis References
    Etiology
    Pathogenesis
    Robust face recognition is a challenging problem, due to facial appearance variations in illumination, pose, expression, aging, partial occlusions and other changes. This paper proposes a novel face recognition approach, where face images are represented by Gabor pixel-pattern-based texture feature (GPPBTF) and local binary pattern (LBP), and null pace-based kernel Fisher discriminant analysis (NKFDA) is applied to the two features independently to obtain two recognition results which are eventually combined together for a final identification. To get GPPBTF, we first transform an image into Gabor magnitude maps of different orientations and scales, and then use pixel-pattern-based texture feature to extract texture features from Gabor maps. In order to improve the final performance of the classification, this paper proposes a multiple NKFDA classifiers combination approach. Extensive experiments on FERET face database demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also achieves more robust result and higher recognition accuracy.
    Local Binary Patterns
    Kernel (algebra)
    Three-dimensional face recognition
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    Endometriosis is defined as the existence of endometrial tissue outside the uterine cavity, and it includes a chronic, inflammatory reaction associated with female infertility and pelvic pain. Endometriosis occurs in 7 to 10% of women. Although it has been studied for more than 50 years, the pathogenesis and development of endometriosis are still poorly understood. There is no curative therapy for endometriosis, which often recurs after surgical or medical treatment. There is a consensus that the adverse current of menstrual blood plays a crucial role in the development of endometriosis. This places a major limitation on research using rodent models of endometriosis, although these are still widely employed, because rodents do not menstruate and endometriosis does not occur spontaneously in these animals. In fact, menstruation and spontaneous endometriosis only occur in women and some non-human primates, making models that employ non-human primates the best animal models for research into the pathogenesis, pathophysiology, spontaneous onset, and treatment of endometriosis. This review assesses the effectiveness and potential of the non-human primate models of endometriosis. It also describes the current findings and theories on the pathogenesis of endometriosis that have been obtained by research using non-human primates.
    Pathogenesis
    Uterine cavity
    Menstruation
    Citations (31)
    Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.
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    Introduction: Endometriosis is a common disease, especially in young women. Inthis article, we reviewed the epidemiology, symptoms, and various options for thetreatment of endometriosis.Material and methods: The work was based on medical articles collected inPubMed, websites and medical books. The research was conducted by looking atkeywords such as: "endometriosis", „endometriosis treatment”, „endometriosis andnew methods"Results: There are many methods of treating endometriosis, ranging fromhormonal therapy to surgical treatment.Conclusions: Endometriosis is difficult disease to treat. We need more researchesand experience to find the perfect way treatment without side effects.
    Medical treatment
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