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

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%).

[1]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  N. Tanaka,et al.  Serum autotaxin levels are correlated with hepatic fibrosis and ballooning in patients with non-alcoholic fatty liver disease , 2018, World journal of gastroenterology.

[3]  Michael Charlton,et al.  The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases , 2018, Hepatology.

[4]  Ukrit Watchareeruetai,et al.  Fat detection algorithm for liver biopsy images , 2014, 2014 International Electrical Engineering Congress (iEECON).

[5]  A. Tzallas,et al.  Deep Learning in Liver Biopsies using Convolutional Neural Networks , 2019, 2019 42nd International Conference on Telecommunications and Signal Processing (TSP).

[6]  Tu Vinh Luong,et al.  Transaminase abnormalities and adaptations of the liver lobule manifest at specific cut-offs of steatosis , 2017, Scientific Reports.

[7]  Joseph Bockhorst,et al.  Automatic classification of white regions in liver biopsies by supervised machine learning. , 2014, Human pathology.

[8]  Cristian Vicas,et al.  Deep convolutional neural nets for objective steatosis detection from liver samples , 2017, 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP).

[9]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Joseph Bockhorst,et al.  Automatic quantification of lobular inflammation and hepatocyte ballooning in nonalcoholic fatty liver disease liver biopsies. , 2015, Human pathology.

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  M. Yoneda,et al.  Current and future pharmacological therapies for NAFLD/NASH , 2017, Journal of Gastroenterology.

[14]  A. Baranova,et al.  Systematic review: the epidemiology and natural history of non‐alcoholic fatty liver disease and non‐alcoholic steatohepatitis in adults , 2011, Alimentary pharmacology & therapeutics.

[15]  Metin Nafi Gürcan,et al.  Quantification of liver fat: A comprehensive review , 2016, Comput. Biol. Medicine.

[16]  M. Laryea,et al.  Management of Recurrent and De Novo NAFLD/NASH After Liver Transplantation. , 2019, Transplantation.

[17]  Y. Kalaidzidis,et al.  3D spatially-resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression , 2019, Nature Medicine.

[18]  Jun Kong,et al.  Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).