A semi-supervised network-based tissue-aware contrast enhancement method for CT images.
10.12122/j.issn.1673-4254.2023.06.14
- Author:
Hao ZHOU
1
;
Dong ZENG
1
;
Zhaoying BIAN
1
;
Jianhua MA
1
Author Information
1. Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
CT image visualization;
computed tomography;
deep learning;
multi-organ segmentation
- MeSH:
Learning;
Image Enhancement;
Tomography, X-Ray Computed
- From:
Journal of Southern Medical University
2023;43(6):985-993
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks.
METHODS:The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs.
RESULTS:The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%.
CONCLUSION:The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.
