Segmentation and Registration of the Liver in Dynamic Contrast-Enhanced Computed Tomography Images
| Autor: | Guangying Ruan, Ling Zhan, Haitao Dai, Shuchao Chen, Sai Li, Lizhi Liu, Shuai Ren, Lin Run, Hongbo Chen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
020901 industrial engineering & automation Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Health Informatics Radiology Nuclear Medicine and imaging Segmentation Dynamic Contrast Enhanced Computed Tomography 02 engineering and technology skin and connective tissue diseases Biomedical engineering |
| Zdroj: | Journal of Medical Imaging and Health Informatics. 11:773-780 |
| ISSN: | 2156-7018 |
| DOI: | 10.1166/jmihi.2021.3327 |
| Popis: | Dynamic contrast-enhanced computed tomography (DCE-CT) is the main auxiliary diagnostic tool for liver diseases. Liver segmentation and registration in all stages of DCE-CT images are the key technology for big data analysis of liver disease diagnosis. The change of imaging conditions in different stages of DCE-CT brings enormous challenges to the segmentation of liver CT images. This study proposes an automatic model for liver segmentation from abdominal CT images in different stages of DCE on the basis of U-Net. The skip connection in U-Net can improve the ability of complex feature recognition. A total of 4863 CT slices from 16 patients with hepatocellular carcinoma (HCC) were selected as the training set, and 1754 CT slices from 6 patients with HCC were selected as the test set. The training and test sets included plain scan, hepatic arterial-dominant phase, and portal venous-dominant phase CT scans. Results showed that the Dice value of the proposed method was significantly higher than those of the full convolutional network and region-growing method. Then, 3D reconstruction and registration were performed on the segmentation results of the liver region of DCE-CT images. The proposed method obtained the best performance, which can provide technical support for the big data analysis of liver diseases. |
| Databáze: | OpenAIRE |
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