Browsing by Author "Komorowski, Richard"

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    Automatic classification of white regions in liver biopsies by supervised machine learning
    (Elsevier, 2014-04) Vanderbeck, Scott; Bockhorst, Joseph; Komorowski, Richard; Kleiner, David E.; Gawrieh, Samer; Medicine, School of Medicine
    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.
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    Automatic quantification of lobular inflammation and hepatocyte ballooning in nonalcoholic fatty liver disease liver biopsies
    (Elsevier, 2015) Vanderbeck, Scott; Bockhorst, Joseph; Kleiner, David; Komorowski, Richard; Chalasani, Naga; Gawrieh, Samer; Department of Medicine, IU School of Medicine
    Automatic quantification of cardinal histologic features of nonalcoholic fatty liver disease (NAFLD) may reduce human variability and allow continuous rather than semiquantitative assessment of injury. We recently developed an automated classifier that can detect and quantify macrosteatosis with greater than or equal to 95% precision and recall (sensitivity). Here, we report our early results on the classifier's performance in detecting lobular inflammation and hepatocellular ballooning. Automatic quantification of lobular inflammation and ballooning was performed on digital images of hematoxylin and eosin–stained slides of liver biopsy samples from 59 individuals with normal liver histology and varying severity of NAFLD. Two expert hepatopathologists scored liver biopsies according the nonalcoholic steatohepatitis clinical research network scoring system and provided annotations of lobular inflammation and hepatocyte ballooning on the digital images. The classifier had precision and recall of 70% and 49% for lobular inflammation, and 91% and 54% for hepatocyte ballooning. In addition, the classifier had an area under the curve of 95% for lobular inflammation and 98% for hepatocyte ballooning. The Spearman rank correlation coefficient for comparison with pathologist grades was 45.2% for lobular inflammation and 46% for hepatocyte ballooning. Our novel observations demonstrate that automatic quantification of cardinal NAFLD histologic lesions is feasible and offer promise for further development of automatic quantification as a potential aid to pathologists evaluating NAFLD biopsies in clinical practice and clinical trials.