Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm

Abstract

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.

Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats

“…A recent study showed automated quantification of liver fibrosis in mice using a segmentation algorithm, U-net, with two magnifications (10× and 40×) of picrosirius red-stained slide images. 19 The study showed a relatively high F1 score (0.8775), which is a value for evaluating model performance, similar to mAP, and a higher correlation with pathologists’ diagnoses at high magnification than with low magnification. Unlike previous studies, we conducted pixel-level detection of hepatic fibrosis in H&E-stained slides obtained from SD rats using the deep-learning instance segmentation algorithm, Mask R-CNN.…”

“…Previous studies applied the image classification method to analyze whether deep learning can achieve the pathologist’s grading accuracy in liver fibrosis using the rodent nonalcoholic steatohepatitis model 18 via a classification algorithm; moreover, these studies attempted to quantify hepatic fibrosis using segmentation in picrosirius red-stained slides. 19 Hepatic fibrosis occurs because of an abnormal and repeated tissue repair response generated as a result of multifactorial chronic liver injury, and its pathogenesis is associated with elevated levels of reactive oxidative stress caused by the metabolism and detoxification of drugs in the liver. 20 N-Nitrosodimethylamine (NDMA), a well-known carcinogen, has been administered to rats to induce hepatic fibrosis; this has been a good and reproducible animal model to investigate the early events involved in the pathogenesis of human liver fibrosis.…”

[Retracted] Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning

“…Monga et al used CNN (Convective Neural Network) to generate binary hash codes for fast image retrieval, and the accuracy rate reached 89% in the data set test [ 21 ]. Ramot et al put forward a network pruning strategy [ 22 ], which starts with pre-training the model, then replaces the parameters below a certain threshold with zeros to form a sparse matrix, and finally trains the sparse CNN. Luo and Li put forward a classic CNN framework, which shows a significant improvement in image classification tasks compared with previous methods [ 23 ].…”

Application of convolutional neural network for analyzing hepatic fibrosis in mice

“…Prior to applying the appropriate statistical methods, the Shapiro-Wilk normality test was performed as the n value of each group was less than 30. Since none of the groups passed, the nonparametric Mann-Whitney test (two-tailed, confidence intervals 95%) was performed for each pair of groups 29 . For all cases, a P value less than 0.05 was considered statistically significant.…”