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Title: Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans
Authors: Donlapark Ponnoprat
Papangkorn Inkeaw
Jeerayut Chaijaruwanich
Patrinee Traisathit
Patumrat Sripan
Nakarin Inmutto
Wittanee Na Chiangmai
Donsuk Pongnikorn
Imjai Chitapanarux
Keywords: Computer Science
Issue Date: 1-Oct-2020
Abstract: © 2020, International Federation for Medical and Biological Engineering. Liver and bile duct cancers are leading causes of worldwide cancer death. The most common ones are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). Influencing factors and prognosis of HCC and ICC are different. Precise classification of these two liver cancers is essential for treatment and prevention plans. The aim of this study is to develop a machine-based method that differentiates between the two types of liver cancers from multi-phase abdominal computerized tomography (CT) scans. The proposed method consists of two major steps. In the first step, the liver is segmented from the original images using a convolutional neural network model, together with task-specific pre-processing and post-processing techniques. In the second step, by looking at the intensity histograms of the segmented images, we extract features from regions that are discriminating between HCC and ICC, and use them as an input for classification using support vector machine model. By testing on a dataset of labeled multi-phase CT scans provided by Maharaj Nakorn Chiang Mai Hospital, Thailand, we have obtained 88% in classification accuracy. Our proposed method has a great potential in helping radiologists diagnosing liver cancer.
ISSN: 17410444
Appears in Collections:CMUL: Journal Articles

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