Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Non-Alcoholic Fatty Liver Disease (NAFLD) is a common syndrome that mainly leads to fat accumulation in liver and steatohepatitis. It is targeted as a severe medical condition ranging from 20% to 40% in adult populations of the Western World. Its effect is identified through insulin resistance, which places patients at high mortality rates. An increased fat aggregation rate, can dramatically increase the development of liver steatosis, which in later stages may advance into fibrosis and cirrhosis. During recent years, new studies have focused on building new methodologies capable of detecting fat cells, based on the histology method with digital image processing techniques. The current study, expands previous work on the detection of fatty liver, by identifying once more a number of diverse histological findings. It is a combined study of both image analysis and supervised learning of fat droplet features, with a specific goal to exclude other findings from fat ratio calculation. The method is evaluated in a total set of 40 liver biopsy images with different magnification capabilities, performing satisfyingly (1.95% absolute error)

Complexity of ballooned hepatocyte feature recognition: Defining a training atlas for artificial intelligence-based imaging in NAFLD

BACKGROUND & AIMS: Histologically assessed hepatocyte ballooning is a key feature discriminating non-alcoholic steatohepatitis (NASH) from steatosis (NAFL). Reliable identification underpins patient inclusion in clinical trials and serves as a key regulatory-approved surrogate endpoint for drug efficacy. High inter/intra-observer variation in ballooning measured using the NASH CRN semi-quantitative score has been reported yet no actionable solutions have been proposed. METHODS: A focused evaluation of hepatocyte ballooning recognition was conducted. Digitized slides were evaluated by 9 internationally recognized expert liver pathologists on 2 separate occasions: each pathologist independently marked every ballooned hepatocyte and later provided an overall non-NASH NAFL/NASH assessment. Interobserver variation was assessed and a \u27concordance atlas\u27 of ballooned hepatocytes generated to train second harmonic generation/two-photon excitation fluorescence imaging-based artificial intelligence (AI). RESULTS: The Fleiss kappa statistic for overall interobserver agreement for presence/absence of ballooning was 0.197 (95% CI 0.094-0.300), rising to 0.362 (0.258-0.465) with a ≥5-cell threshold. However, the intraclass correlation coefficient for consistency was higher (0.718 [0.511-0.900]), indicating \u27moderate\u27 agreement on ballooning burden. 133 ballooned cells were identified using a ≥5/9 majority to train AI ballooning detection (AI-pathologist pairwise concordance 19-42%, comparable to inter-pathologist pairwise concordance of between 8-75%). AI quantified change in ballooned cell burden in response to therapy in a separate slide set. CONCLUSIONS: The substantial divergence in hepatocyte ballooning identified amongst expert hepatopathologists suggests that ballooning is a spectrum, too subjective for its presence or complete absence to be unequivocally determined as a trial endpoint. A concordance atlas may be used to train AI assistive technologies to reproducibly quantify ballooned hepatocytes that standardize assessment of therapeutic efficacy. This atlas serves as a reference standard for ongoing work to refine how ballooning is classified by both pathologists and AI. LAY SUMMARY: For the first time, we show that, even amongst expert hepatopathologists, there is poor agreement regarding the number of ballooned hepatocytes seen on the same digitized histology images. This has important implications as the presence of ballooning is needed to establish the diagnosis of non-alcoholic steatohepatitis (NASH), and its unequivocal absence is one of the key requirements to show \u27NASH resolution\u27 to support drug efficacy in clinical trials. Artificial intelligence-based approaches may provide a more reliable way to assess the range of injury recorded as hepatocyte ballooning

Quantitative Analysis and Monte Carlo Modeling of Fat-Mediated MRI Relaxation

Hepatic steatosis is the accumulation of fat in the liver, affecting about 25% of the world population. Steatosis can cause lipo-toxicity and eventually lead to fibrosis, cirrhosis and ultimately liver failure if timely interventions are not provided. So, early diagnosis and disease monitoring of steatosis is crucial to reduce morbidity and mortality.Chemical shift based Magnetic Resonance Imaging (MRI) techniques using single and dual R2*(transverse relaxation rate) models have been reported to quantify fat fraction (FF) for assessment of steatosis. However, there is no common consensus between these two models and current data is limited for which model is accurate to quantify FF. Fully characterizing the behavior of the modelsover the entire clinical range of hepatic steatosis is essential to determine the limits of each of the models. However, performing a systematic investigation of the R2*models in patient population is infeasible. This thesis presents a computational approach by building a Monte Carlo based model as an alternative way to examine the R2*-MRI models. A 3D liver volume with impenetrable fat spheres was simulated to mimic hepatic steatosis. The simulation of steatosis was done using realistic data obtained from automatic segmentation and characterization of fat droplets using liver biopsy images. MRI signals were synthesized in the virtual liver volume using Monte Carlo modeling approach. Finally, the R2*behavior was analyzed using both the single and dual R2*models and they were compared against in-vivo calibration to determine their accuracy. Predicted R2*values were within confidence bounds of the published in vivo calibration and single R2*modelshowed higher accuracy than dual R2*model to estimate FF. In conclusion, this research developed a computational framework for creating realistic hepatic steatosis model and synthesizing MRI signal and analyzing R2*behavior in the presence of fat. The developed computational methods will also be generalizable to create other tissue-specific models and study R2*behavior at higher field strengths, for testing new MRI pulse sequences and in presence of other co-existing pathologies such as hepatic iron overload

A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH

BACKGROUND AND AIMS: Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response. APP ROA CH AND RESULT S: Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression. CONCLUSIONS: Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies. (Hepatology 2021;74:133-147)

Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images

Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones

Investigation of hypothermic machine perfusion of human donor livers for improved organ preservation: measurements of organ quality and safety

Introduction Hypothermic machine perfusion (HMP) could improve the outcome of marginal liver transplantation, but the optimal perfusion setup and injury markers are unknown. The null hypothesis herein is no difference in cellular and mitochondrial injury during ex-situ preservation when comparing SCS and end-ischaemic HMP. Methods This is a single-centre, randomised study of end-ischaemic HMP in discarded human livers. A total of 45 livers were preserved for 4 hours with static cold storage (n = 7), arterial perfusion (n = 10), non-oxygen supplemented venous perfusion (n = 17), and oxygen supplemented venous perfusion (n = 11). Dynamic, biochemical, morphological, and mitochondrial parameters were analysed. Additionally, oxygenation kinetics and steatosis assessment were examined. Results Arterial perfusion resulted in higher resistance and lower flow compared with venous perfusion (p ≤ 0.01), as well as higher perfusate transaminases in the former group (p > 0.05). High-risk marginal livers were associated with 2-fold higher perfusate transaminases (p > 0.05) and higher post-preservation mitochondrial complex II-III activity (p = 0.01) compared to low-risk livers. Morphology and mitochondrial function were maintained in all groups and oxygenation did not trigger oxidative injury. Parenchymal oxygen measurement indicated evidence of oxygen consumption. A revised steatosis grading system using digital image analysis was accurate and showed high agreement with standard H&E assessment. Conclusion There was not enough evidence to reject the null hypothesis. Arterial-only perfusion might be inadequate for liver preservation based on the limited perfusate supply, but randomised trials are needed to determine the requirement of arterial perfusion in dual-vessel perfusion machines. The sensitivity of highrisk livers to ischaemia reperfusion injury might be reflected in their mitochondrial function, which needs to be assessed in future. Perfusate oxygenation is safe but the optimal perfusate oxygen remains unknown. DIA is a promising method, which can standardise steatosis evaluation

Liver Biopsy

Liver biopsy is recommended as the gold standard method to determine diagnosis, fibrosis staging, prognosis and therapeutic indications in patients with chronic liver disease. However, liver biopsy is an invasive procedure with a risk of complications which can be serious. This book provides the management of the complications in liver biopsy. Additionally, this book provides also the references for the new technology of liver biopsy including the non-invasive elastography, imaging methods and blood panels which could be the alternatives to liver biopsy. The non-invasive methods, especially the elastography, which is the new procedure in hot topics, which were frequently reported in these years. In this book, the professionals of elastography show the mechanism, availability and how to use this technology in a clinical field of elastography. The comprehension of elastography could be a great help for better dealing and for understanding of liver biopsy

Ensemble convolutional neural network classification for pancreatic steatosis assessment in biopsy images

Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians

Quantification of liver fibrosis—a comparative study

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic