使用机器学习和形态学成像测量肝脏活检中的脂肪变性

2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) Pub Date : 2017-06-22 DOI:10.1109/CBMS.2017.98

N. Giannakeas, M. Tsipouras, A. Tzallas, M. G. Vavva, Maria Tsimplakidou, E. Karvounis, R. Forlano, P. Manousou

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引用次数: 3

摘要

非酒精性肝病(NAFLD)是当今西方国家最常见的肝病。这是肝脏脂肪扩张的慢性疾病，与饮酒无关。定量肝活检中的脂肪变性可以提供疾病严重程度的客观测量，而不是使用半定量评分系统。目前的工作，介绍了一种自动化的方法来测量肝脏活检中的脂肪变性，使用机器学习和经典的图像处理技术。聚类用于组织标本检测，而迭代形态学过程用于脂肪变性揭示。该方法已在一组20张肝活检图像中进行了评估，所获得的结果存在1%的平均百分比误差。

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Measuring Steatosis in Liver Biopsies Using Machine Learning and Morphological Imaging

Non-Alcohol Liver Disease (NAFLD) is nowadays the most common liver disease in Western Countries. It is the chronic condition of fat expansion in liver, which is not associated with alcohol consumption. Quantitating steatosis in liver biopsies could provide objective measurement of the severity of the disease, instead of using semi-quantitative scoring systems. The current work, introduces an automated method for measuring steatosis in liver biopsies, using both machine learning and classical image processing techniques. Clustering is employed for tissue specimen detection, while an iterative morphological procedure is used for steatosis revealing. The method has been evaluated in a set of 20 liver biopsy images and the obtained results present ∼1% mean percentage error.

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2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)

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