Browsing by Author "Morusu, Siripriya"

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    Automated assessment of steatosis in murine fatty liver
    (PLOS, 2018-05-10) Sethunath, Deepak; Morusu, Siripriya; Tuceryan, Mihran; Cummings, Oscar W.; Zhang, Hao; Yin, Xiao-Ming; Vanderbeck, Scott; Chalasani, Naga; Gawrieh, Samer; Computer and Information Science, School of Science
    Although mice are commonly used to study different aspects of fatty liver disease, currently there are no validated fully automated methods to assess steatosis in mice. Accurate detection of macro- and microsteatosis in murine models of fatty liver disease is important in studying disease pathogenesis and detecting potential hepatotoxic signature during drug development. Further, precise quantification of macrosteatosis is essential for quantifying effects of therapies. Here, we develop and validate the performance of automated classifiers built using image processing and machine learning methods for detection of macro- and microsteatosis in murine fatty liver disease and study the correlation of automated quantification of macrosteatosis with expert pathologist’s semi-quantitative grades. The analysis is performed on digital images of 27 Hematoxylin & Eosin stained murine liver biopsy samples. An expert liver pathologist scored the amount of macrosteatosis and also annotated macro- and microsteatosis lesions on the biopsy images using a web-application. Using these annotations, supervised machine learning and image processing techniques, we created classifiers to detect macro- and microsteatosis. For macrosteatosis prediction, the model’s precision, sensitivity and area under the receiver operator characteristic (AUROC) were 94.2%, 95%, 99.1% respectively. When correlated with pathologist’s semi-quantitative grade of steatosis, the model fits with a coefficient of determination value of 0.905. For microsteatosis prediction, the model has precision, sensitivity and AUROC of 79.2%, 77%, 78.1% respectively. Validation by the expert pathologist of classifier’s predictions made on unseen images of biopsy samples showed 100% and 63% accuracy for macro- and microsteatosis, respectively. This novel work demonstrates that fully automated assessment of steatosis is feasible in murine liver biopsies images. Our classifier has excellent sensitivity and accuracy for detection of macrosteatosis in murine fatty liver disease.
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    Automated Methods To Detect And Quantify Histological Features In Liver Biopsy Images To Aid In The Diagnosis Of Non-Alcoholic Fatty Liver Disease
    (2016-03-31) Morusu, Siripriya; Tuceryan, Mihran; Zheng, Jiang; Tsechpenakis, Gavriil; Fang, Shiaofen
    The ultimate goal of this study is to build a decision support system to aid the pathologists in diagnosing Non-Alcoholic Fatty Liver Disease (NAFLD) in both adults and children. The disease is caused by accumulation of excess fat in liver cells. It is prevalent in approximately 30% of the general population in United States, Europe and Asian countries. The growing prevalence of the disease is directly related to the obesity epidemic in developed countries. We built computational methods to detect and quantify the histological features of a liver biopsy which aid in staging and phenotyping NAFLD. Image processing and supervised machine learning techniques are predominantly used to develop a robust and reliable system. The contributions of this study include development of a rich web interface for acquiring annotated data from expert pathologists, identifying and quantifying macrosteatosis in rodent liver biopsies as well as lobular inflammation and portal inflammation in human liver biopsies. Our work on detection of macrosteatosis in mouse liver shows 94.2% precision and 95% sensitivity. The model developed for lobular inflammation detection performs with precision and sensitivity of 79.3% and 81.3% respectively. We also present the first study on portal inflammation identification with 82.1% precision and 88.3% sensitivity. The thesis also presents results obtained for correlation between model computed scores for each of these lesions and expert pathologists' grades.