Fat Droplets Identification in Liver Biopsies using Supervised Learning Techniques

Arjmand, Alexandros; Tzallas, Alexandros T.; Tsipouras, Markos G.; Forlano, Roberta; Manousou, Pinelopi; Katertsidis, Nikolaos S.; Γιαννακέας, Νικόλαος

doi:10.1145/3197768.3201554

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…At the present time, ballooning degeneration is at the heart of the clinical interest among research pathologists, as its assessment emerges as a critical factor in chronic liver diseases. Thus, the proposed work could be also considered as a follow-up of a previous study [20] for ballooned cell identification and ballooning degeneration quantification.…”

Section: Resultsmentioning

confidence: 98%

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Arjmand

Γιαννακέας

2018

Eng. Technol. Appl. Sci. Res.

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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).

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Section: Resultsmentioning

confidence: 98%

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Arjmand

Γιαννακέας

2018

Eng. Technol. Appl. Sci. Res.

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“…All these performances, along with the error rates, ultimately produce a classification accuracy of up to 95%. It is noted that they show an improvement compared to the results of previous classification approaches [29] and is expected that they will further reduce the overall fat and ballooning prevalence ratio error compared to human visual interpretations [30]. It is also important, that current outcomes suggest a steady improvement in automated detection techniques and emphasize their diagnostic capabilities with respect to semi-quantitative methods.…”

Section: Testing Performancementioning

confidence: 99%

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

et al. 2019

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Nonalcoholic fatty liver disease (NAFLD) is responsible for a wide range of pathological disorders. It is characterized by the prevalence of steatosis, which results in excessive accumulation of triglyceride in the liver tissue. At high rates, it can lead to a partial or total occlusion of the organ. In contrast, nonalcoholic steatohepatitis (NASH) is a progressive form of NAFLD, with the inclusion of hepatocellular injury and inflammation histological diseases. Since there is no approved pharmacotherapeutic solution for both conditions, physicians and engineers are constantly in search for fast and accurate diagnostic methods. The proposed work introduces a fully automated classification approach, taking into consideration the high discrimination capability of four histological tissue alterations. The proposed work utilizes a deep supervised learning method, with a convolutional neural network (CNN) architecture achieving a classification accuracy of 95%. The classification capability of the new CNN model is compared with a pre-trained AlexNet model, a visual geometry group (VGG)-16 deep architecture and a conventional multilayer perceptron (MLP) artificial neural network. The results show that the constructed model can achieve better classification accuracy than VGG-16 (94%) and MLP (90.3%), while AlexNet emerges as the most efficient classifier (97%).

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Fat Droplets Identification in Liver Biopsies using Supervised Learning Techniques

Cited by 2 publications

References 11 publications

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Fat Quantitation in Liver Biopsies Using a Pretrained Classification Based System

Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples

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