Super-resolution reconstruction of pneumocystis carinii pneumonia images based on generative confrontation network. (March 2022)

• Record Type: Journal Article
• Authors: Shi, Jianshe; Ye, Yuguang; Liu, Huifang; Zhu, Daxin; Su, Lianta; Chen, Yijie; Huang, Yifeng; Huang, Jianlong
• Is Part Of: Computer methods and programs in biomedicine. Volume 215(2022)
• Publication Date: 2022-03
• Subjects: Pneumocystis carinii pneumonia -- Super-resolution reconstruction -- Generative adversarial network -- Dense convolutional network -- Texture loss; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106578
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: PCP is an interstitial plasma cell pneumonia. Quality of PCP's high-resolution CT influences prognosis of patients. Super-resolution reconstruction uses generative counter-networks in diagnostics. VGG19 network is the framework of discriminant network. WGAN-GP optimizes network model to accelerate convergence of loss. Abstract: Objective: Pneumocystis carinii pneumonia, also known as pneumocystis carinii pneumonia (PCP), is an interstitial plasma cell pneumonia caused by pneumocystis spp. It is a conditional lung infectious disease. Because the early and correct diagnosis of PCP has a great influence on the prognosis of patients, the image processing of PCP's high-resolution CT (HRCT) is extremely important. Traditional image super-resolution reconstruction algorithms have difficulties in network training and artifacts in generated images. The super-resolution reconstruction algorithm of generative counter-networks can optimize these two problems well. Methods: In this paper, the texture enhanced super-resolution generative adversarial network (TESRGAN) is based on a generative confrontation network, which mainly includes a generative network and a discriminant network. In order to improve the quality of image reconstruction, TESRGAN improved the structure of the Super-Resolution Generative Adversarial Network (SRGAN) generation network, removed all BN layers in SRGAN, and replaced the ReLU function with the LeakyReLU function as the nonlinear activation function of the network to avoid the disappearance of the gradient. Experimental results: The TESRGAN algorithm in this paper is compared with the image reconstruction results of Bicubic, SRGAN, Enhanced Deep Super-Resolution network (EDSR), and ESRGAN. Compared with algorithms such as SRGAN and EDSR, our algorithm has clearer texture details and more accurate brightness information without extending the running time. Our reconstruction algorithm can improve the accuracy of image low-frequency information. Conclusion: The texture details of the reconstruction result are clearer and the brightness information is more accurate, which is more in line with the requirements of visual sensory evaluation.