| Autor: |
Benahmed, Amina, Khemis, Kamila, Salim, Loudjedi |
| Zdroj: |
International Journal of Biomedical Engineering and Technology; 2024, Vol. 44 Issue: 2 p103-119, 17p |
| Abstrakt: |
The vascular mapping of liver is part of personalised medicine which allows optimal management of the patient during surgery. The work that we propose allows 3D reconstruction of the liver with its vascular tree. The main steps followed to achieve this processing are image pre-processing based on Hounsfield windowing, automatic segmentation of liver, then vascular tree extraction. Liver automatic segmentation is a challenging stage because of the inter-patient variability of the liver shape and its similar grey level with neighbouring organs. The deep learning approach fits this problem. We applied fully convolutional networks (FCN) 'U-Net' with 38 layers, 40 connections, Adam's algorithm optimiser and learning rate (LR) of 0.001. We tested this model on two CT scan databases: 3Dircadb and Task08_HepaticVessels. The results obtained, validated quantitatively, are very satisfactory (dice coefficient 98% and loss function 2%), comparable to the literature and confirm the robustness of U-Net for automatic liver segmentation. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
