Automatic classification of white regions in liver biopsies by supervised machine learning

Abstract

Automated assessment of histological features of non-alcoholic fatty liver disease (NAFLD) may reduce human variability and provide continuous rather than semiquantitative measurement of these features. As part of a larger effort, we perform automatic classification of steatosis, the cardinal feature of NAFLD, and other regions that manifest as white in images of hematoxylin and eosin-stained liver biopsy sections. These regions include macrosteatosis, central veins, portal veins, portal arteries, sinusoids and bile ducts. Digital images of hematoxylin and eosin-stained slides of 47 liver biopsies from patients with normal liver histology (n = 20) and NAFLD (n = 27) were obtained at 20× magnification. The images were analyzed using supervised machine learning classifiers created from annotations provided by two expert pathologists. The classification algorithm performs with 89% overall accuracy. It identified macrosteatosis, bile ducts, portal veins and sinusoids with high precision and recall (≥ 82%). Identification of central veins and portal arteries was less robust but still good. The accuracy of the classifier in identifying macrosteatosis is the best reported. The accurate automated identification of macrosteatosis achieved with this algorithm has useful clinical and research-related applications. The accurate detection of liver microscopic anatomical landmarks may facilitate important subsequent tasks, such as localization of other histological lesions according to liver microscopic anatomy.

A Novel Automatic Digital Algorithm that Accurately Quantifies Steatosis in NAFLD on Histopathological Whole‐Slide Images

“…This observation, as well as the absolute percentages measured with our algorithm, corroborates findings from the other DIA methods quantifying steatosis (14,16,18,20,30,33). This observation, as well as the absolute percentages measured with our algorithm, corroborates findings from the other DIA methods quantifying steatosis (14,16,18,20,30,33).…”

Automatic quantification of lobular inflammation and hepatocyte ballooning in nonalcoholic fatty liver disease liver biopsies

“…While the overall test statistics for correlation with pathologist grade are not as high as those our research group has shown for steatosis grading (12,18), they show a general concordance between the model scores and pathologist grades and demonstrate the feasibility of such an approach. While our ultimate goals are to replace discrete grades with continuous measures of lesions, we thought it important to show the general relationship between continuous measures and pathologist grade.…”

“…The aim of this initial study is to determine if an automated tool utilizing supervised machine learning could be trained by pathologists to detect lobular inflammation and hepatocyte ballooning. Our group has previously published research demonstrating the feasibility of the accurate categorization of the white regions in liver biopsy images including macro-steatosis, central veins, portal veins, portal arteries, sinusoids and bile ducts (18). To date, no previous work has set out to automatically quantify lobular inflammation and hepatocyte ballooning.…”

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

“…In supervised learning, tools learn to output the correct labeled target, which can vary from detection of underlying liver disease in patients, early detection of nonalcoholic fatty liver disease (NAFLD) with images, or better identification of patients with primary sclerosing cholangitis (PSC) at risk for hepatic decompensation (HD) or otherwise (Fig. ) . ML in the supervised setting encompasses tools that can uncover nonlinear patterns in the data to predict these various output targets.…”