Browsing by Author "Demetrick, Douglas James"

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    DNA Methylation Landscape of the Fibrinogen Gene Cluster in the Equine Embryo
    (2018-09-26) Grant, Danielle Magann; Klein, C.; Riabowol, Karl T.; Demetrick, Douglas James
    The initial weeks of equine pregnancy includes the unique phase of embryo mobility from day 9 until fixation on day 15. Fixation of the embryo to the uterine wall occurs before a microvillous attachment at ~day 40. Throughout the mobility period the equine embryo expresses and secretes fibrinogen, a protein best known for its involvement in the coagulation cascade and wound repair. The aim of this MSc project was to contribute to the characterization of conceptus-derived fibrinogen at the time of fixation. We confirmed early embryo expression in the absence of hepatic activity, as well as describe expression by the later fetal-placenta. We characterized DNA methylation across the fibrinogen gene cluster for equine liver, day 14 embryos, and endometrium. The methylation landscape of equine embryos is distinct from both liver and endometrium. However, in key regulatory regions the embryo and liver profiles were the same. The similarity to liver methylation in known regulatory regions supports fibrinogen expression by the embryo and suggests its involvement in gene regulation. In addition to our genetic characterization, we trialed various in vitro assays in an attempt to determine the possible role of fibrinogen at the embryo-maternal interface. Overall this study has contributed to our ongoing effort to provide context for the novel extra-hepatic expression of fibrinogen by the pre-implantation conceptus.
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    Intelligent Medical Image Analysis for Quality Assurance, Teaching and Evaluation
    (2020-06-23) Aksac, Alper; Alhajj, Reda; Demetrick, Douglas James; Rokne, Jon G.; Moshirpour, Mohammad; Karray, Fakhreddine O.
    Manually spotting and annotating the affected area(s) on histopathological images with high accuracy is regarded as the gold standard in cancer diagnosis and grading. However, this is a time-consuming and tedious task that requires considerable effort, expertise and experience of a pathologist. These are gained over time by analyzing more cases. Whereas this visual interpretation has strict guidelines. This brings a certain subjectivity to the histological analysis, and therefore, leads to inter/intra-observer variability and some reproducibility issues. Besides, these issues may have a direct effect on patient prognosis and treatment plan. These problems can be alleviated by developing automated image analysis tools for digitized histopathology. Thanks to the rapid development in the image capturing and analysis technology which could be employed to not only give more insight to pathologists, but also guide them in detecting and grading diseases. These quantitative computational tools aim to improve the quality of pathology researchers in terms of speed and accuracy. Thus, it is very important to develop an automatic assessment tool for quantitative and qualitative analysis to help remove this drawback. The main contribution of this thesis is an intelligent system for quality assurance, teaching and evaluation applications in anatomical pathology. We present a spatial clustering algorithm, named CutESC (Cut-Edge for Spatial Clustering) with a graph-based approach. CutESC performs clustering automatically for complicated shapes and different density without requiring any prior information and parameters. We have developed an automatic cell nuclei detection method where the proposed solution uses the traditional CNN learning scheme solely to detect nuclei, and then applies single-pass voting with spatial clustering explicitly to detect them. We also propose an automated method to identify and locate the mitotic cells, and tubules in histopathology images using deep neural network frameworks. We present a dataset of breast cancer histopathology images named BreCaHAD which is publicly available to the biomedical imaging community. Moreover, we propose an efficient method for salient region detection. Finally, we introduce a new tool called CACTUS (Cancer Image Annotating, Calibrating, Testing, Understanding and Sharing) which is proposed to help and guide pathologists in their effort to improve disease diagnosis and thereby reduce their workload and bias among them. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.