Automated Methods To Detect And Quantify Histological Features In Liver Biopsy Images To Aid In The Diagnosis Of Non-Alcoholic Fatty Liver Disease

dc.contributor.advisorTuceryan, Mihran
dc.contributor.authorMorusu, Siripriya
dc.contributor.otherZheng, Jiang
dc.contributor.otherTsechpenakis, Gavriil
dc.contributor.otherFang, Shiaofen
dc.date.accessioned2016-09-06T20:23:33Z
dc.date.available2016-09-06T20:23:33Z
dc.date.issued2016-03-31
dc.degree.date2016en_US
dc.degree.grantorPurdue Universityen_US
dc.degree.levelM.S.en_US
dc.descriptionIndiana University-Purdue University Indianapolis (IUPUI)en_US
dc.description.abstractThe 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.en_US
dc.identifier.doi10.7912/C2601P
dc.identifier.urihttps://hdl.handle.net/1805/10858
dc.identifier.urihttp://dx.doi.org/10.7912/C2/2329
dc.language.isoen_USen_US
dc.subjectLiver Disease Diagnosisen_US
dc.subjectMachine Learningen_US
dc.subjectImage Processingen_US
dc.titleAutomated Methods To Detect And Quantify Histological Features In Liver Biopsy Images To Aid In The Diagnosis Of Non-Alcoholic Fatty Liver Diseaseen_US
dc.typeThesisen
thesis.degree.disciplineComputer & Information Scienceen
thesis.degree.grantorPurdue Universityen
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