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TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Aghamohammadi, Amirhossein, Ranjbarzadeh, Ramin orcid logoORCID: 0000-0001-7065-9060, Naiemi, Fatemeh, Mogharrebi, Marzieh, Dorosti, Shadi and Bendechache, Malika orcid logoORCID: 0000-0003-0069-1860 (2021) TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach. Expert Systems with Applications, 183 . pp. 115-406. ISSN 0957-4174

Abstract
Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data. Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-of-the-art works in terms of segmentation accuracy and efficiency.
Metadata
Item Type:Article (Published)
Refereed:Yes
Additional Information:Article number: 115406
Uncontrolled Keywords:Image segmentation; Deep learning; Lesion detection; Liver segmentation; Convolutional Neural Network
Subjects:Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Image processing
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > Lero: The Irish Software Engineering Research Centre
Research Initiatives and Centres > ADAPT
Publisher:Elsevier
Official URL:https://doi.org/10.1016/j.eswa.2021.115406
Copyright Information:© 2021 Elsevier (CC-BY-ND)
Funders:Science Foundation Ireland (SFI) 684 under the grants No. 13/RC/2094\_P2 (Lero) and 13/RC/2106\_P2 (ADAPT)
ID Code:26042
Deposited On:29 Jun 2021 12:46 by Malika Bendechache . Last Modified 17 Aug 2022 09:58
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