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- Title
Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.
- Authors
Ponnoprat, Donlapark; Inkeaw, Papangkorn; Chaijaruwanich, Jeerayut; Traisathit, Patrinee; Sripan, Patumrat; Inmutto, Nakarin; Na Chiangmai, Wittanee; Pongnikorn, Donsuk; Chitapanarux, Imjai
- Abstract
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.
- Subjects
LIVER cancer; CHOLANGIOCARCINOMA; BILE ducts; COMPUTED tomography; MACHINE learning
- Publication
Medical & Biological Engineering & Computing, 2020, Vol 58, Issue 10, p2497
- ISSN
0140-0118
- Publication type
Article
- DOI
10.1007/s11517-020-02229-2