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

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 ob-tained 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.

Automated summary

The paper presents a strategy based on cascade convolutional neural network to address the challenges of liver and tumor segmentation. The Z-Score algorithm is used for image normalization and a novel encoding algorithm LDOG is proposed for key feature recognition. A cascade CNN structure is employed to extract local and semi-global features, utilizing a simple but effective model to improve segmentation accuracy and efficiency, outperforming current state-of-the-art works.