Automatic Hepatocellular Carcinoma Grading through Traditional Trabecular and CNN features

Abstract

The accurate grading of Hepatocellular Carcinoma (HCC) in histopathological liver tissue images is crucial to prognosis and treatment planning However the traditional diagnosis process is subjective and time-consuming Accordingly this study proposes a novel method to automatically classify a liver tissue as one of four different tumor grades namely 1(Well differentiated) 2(Moderately differentiated) 3(Poorly differentiated) and (Undifferentiated) In the proposed method a CNN-based feature extractor is trained to retrieve the features of the tissue that are hard to be quantized by traditional method In addition sinusoid cell and trabecular features are extracted by image processing Finally a classifier is constructed using all the features to predict the tumor grade of the input tissue The proposed classifier reaches a 98 2% accuracy on whole slide images (WSIs) of HCC The experimental results show that the proposed method performs well on liver tumor grading prediction and proves that the features obtained from the CNN-based feature extractor and trabecular features can further enhance the performance of the classifier

Date of Award	2019
Original language	English
Supervisor	Pau-Choo Chung (Supervisor)