TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach
| Autor: | Amirhossein Aghamohammadi, Shadi Dorosti, Marzieh Mogharrebi, Ramin Ranjbarzadeh, Fatemeh Naiemi, Malika Bendechache |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Artificial intelligence Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Boundary (topology) 02 engineering and technology Convolutional neural network Fuzzy logic Surgical planning Image (mathematics) 020901 industrial engineering & automation Image processing Artificial Intelligence Encoding (memory) Machine learning 0202 electrical engineering electronic engineering information engineering Segmentation Image segmentation Deep learning Lesion detection Liver segmentation Convolutional Neural Network business.industry General Engineering Pattern recognition Computer Science Applications Path (graph theory) 020201 artificial intelligence & image processing business Algorithms |
| Zdroj: | Aghamohammadi, Amirhossein, Ranjbarzadeh, Ramin ORCID: 0000-0001-7065-9060 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|