Drs. Goceri, Kus, and Senaras Present at OSUMC Research Day

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”.