NC2C-TransCycleGAN: Non-Contrast to Contrast-Enhanced CT Image Synthesis Using Transformer CycleGAN
Journal Title: Healthcraft Frontiers - Year 2024, Vol 2, Issue 1
Abstract
Background: Lung cancer poses a great threat to human life and health. Although the density differences between lesions and normal tissues shown on enhanced CT images is very helpful for doctors to characterize and detect lesions, contrast agents and radiation may cause harm to the health of patients with lung cancer. By learning the mapping relationship between plain CT image and enhanced CT image through deep learning methods, high quality synthetic CECT image results can be generated based on plain scan CT image. It has great potential to help save treatment time and cost of lung cancer patients, reduce radiation dose and expand the medical image dataset in the field of deep learning. Methods: In this study, plain and enhanced CT images of 71 lung cancer patients were retrospectively collected. The data from 58 lung cancer patients were randomly assigned to the training set, and the other 13 cases formed the test set. The Convolution Vison Transformer structure and PixelShuffle operation were combined with CycleGAN, respectively, to help generate clearer images. After random erasing, image scaling and flipping to enhance the training data, paired plain and enhanced CT slices of each patient are input into the network as input and labeled, respectively, for model training. Finally, the peak signal-to-noise ratio, structural similarity and mean square error are used to evaluate the image quality and similarity. Results: The performance of our proposed method is compared with CycleGAN and Pix2Pix on the test set, respectively. The results show that the SSIM value of the enhanced CT images generated by the proposed method improve by 2.00% and 1.39%, the PSNR values improve by 2.05% and 1.71%, and the MSE decreases by 12.50% and 8.53%, respectively, compared with Pix2Pix and CycleGAN. Conclusions: The experimental results show that the improved algorithm based on CylceGAN proposed in this paper can synthesize high-quality lung cancer synthetic enhanced CT images, which is helpful to expand the lung cancer image data set in the deep learning research. More importantly, this method can help lung cancer patients save medical treatment time and cost.
Authors and Affiliations
Xiaoxue Hou, Ruibo Liu, Youzhi Zhang, Xuerong Han, Jiachuan He, He Ma
Keywords
Related Articles
- EP ID EP744645
- DOI https://doi.org/10.56578/hf020104
- Views 24
- Downloads 0
Investigating Malaria Susceptibility in Central Maluku District: A Focus on Anopheles Mosquito Habitats
Malaria remains a formidable challenge to global public health, with an estimated 241 million cases reported across 85 endemic countries in 2020. Within this context, Indonesia, and particularly the Central Maluku Rege...
Enhanced Forecasting of Alzheimer’s Disease Progression Using Higher-Order Circular Pythagorean Fuzzy Time Series
This study introduces an advanced forecasting method, utilizing a higher-order circular Pythagorean fuzzy time series (C-PyFTSs) approach, for the prediction of Alzheimer’s disease progression. Distinct from traditional...
Pneumonia Detection Technique Empowered with Transfer Learning Approach
Detection of normal findings or pneumonia using modern technology has a lot of significance in medical analysis and artificial intelligence. Still, more specifically, its importance increases in deep learning. Deep l...
Unlocking Minds: An Adaptive Machine Learning Approach for Early Detection of Depression
Depression, a prevalent and severe medical condition, significantly impairs emotional well-being, cognitive functions, and behavior, often leading to substantial challenges in daily functioning and, in severe cases,...
Influence of Nutritional Status on Academic Performance: A Study of Schoolchildren in Eastern Morocco
This study investigates the impact of nutritional status on academic performance among schoolchildren in Eastern Morocco. Focusing on the prevalence of overweight, obesity, and their associations with academic outcomes,...