{'fraction_non_alphanumeric': 0.02804986642920748, 'fraction_numerical': 0.016028495102404273, 'mean_word_length': 6.0, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automated image analysis method for detecting and quantifying macrovesicular steatosis in hematoxylin and eosin–stained histology images of human livers

Large‐droplet macrovesicular steatosis (ld‐MaS) in more than 30% of liver graft hepatocytes is a major risk factor for liver transplantation. An accurate assessment of the ld‐MaS percentage is crucial for determining liver graft transplantability, which is currently based on pathologists' evaluations of hematoxylin and eosin (H&E)–stained liver histology specimens, with the predominant criteria being the relative size of the lipid droplets (LDs) and their propensity to displace a hepatocyte's nucleus to the cell periphery. Automated image analysis systems aimed at objectively and reproducibly quantifying ld‐MaS do not accurately differentiate large LDs from small‐droplet macrovesicular steatosis and do not take into account LD‐mediated nuclear displacement; this leads to a poor correlation with pathologists' assessments. Here we present an improved image analysis method that incorporates nuclear displacement as a key image feature for segmenting and classifying ld‐MaS from H&E‐stained liver histology slides. 52,000 LDs in 54 digital images from 9 patients were analyzed, and the performance of the proposed method was compared against the performance of current image analysis methods and the ld‐MaS percentage evaluations of 2 trained pathologists from different centers. We show that combining nuclear displacement and LD size information significantly improves the separation between large and small macrovesicular LDs (specificity = 93.7%, sensitivity = 99.3%) and the correlation with pathologists' ld‐MaS percentage assessments (linear regression coefficient of determination = 0.97). This performance vastly exceeds that of other automated image analyzers, which typically underestimate or overestimate pathologists' ld‐MaS scores. This work demonstrates the potential of automated ld‐MaS analysis in monitoring the steatotic state of livers. The image analysis principles demonstrated here may help to standardize ld‐MaS scores among centers and ultimately help in the process of determining liver graft transplantability. Liver Transpl 20:228‐236, 2014. © 2013 AASLD.