On April 25th, Drs. Goceri, Kus, and Senaras presented their research at the OSUMC Research Day.
Dr. Evgin Goceri presented “Automatic and Robust Segmentation of Liver
and Its Vessels from MR Datasets for Pre-Evaluation of Liver Translation” which
proposed a robust and fully automated method for segmenting the liver and its
vessels from MR images. Dr. Goceri’s study presented a novel approach to
reducing processing time by employing binary regularization of the level set
function. The fully-automatic segmentation of liver and its vessels
with the proposed method was more efficient than manual approach and the other
methods in the literature in terms of processing time and accuracy.
Dr. Pelin Kus presented “Segmentation and Quantification of Tissue
Necrosis in Tuberculosis” which focuses on how the immune system of patients
infected with M. tuberculosis
responds by using many types of cells including macrophages that form
granulomas within the pulmonary tissue. Segmentation and quantification of the
necrosis often formed in the center of the granuloma will enable experts to
understand the relationship between
genes, molecules, and cells that contribute to granuloma and/or lung necrosis
and to better identify and treat patients infected with M. tuberculosis. Dr. Kus’s study developed an algorithm to quantify
and delineate necrotic areas from histopathological slides for the tuberculosis
disease. The promising results of this study indicate the possibility of using
such an algorithm for large scale analysis.
Dr. Caglar Senaras presented “Automated Otoscopy to Diagnose
Ear Pathology” which focused on methods for identifying ear infections. His
project addressed the need to improve diagnostic accuracy by developing an
objective method to assess the eardrum. Objective methods to identify eardrum
abnormalities would assist clinicians in diagnosing or ruling out pathologies
that may be subtle on clinical otoscopy (e.g., middle ear fluid). In Dr.
Senaras’s study, a computer-based image analysis approach was proposed which
can objectively determine whether an eardrum is “normal” or “abnormal”.
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