The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.

Although several features of apoptosis and autophagy have been reported in the larval organs of Lepidoptera during metamorphosis, solid experimental evidence for autophagy is still lacking. Moreover, the role of the two processes and the nature of their relationship are still cryptic. In this study, we perform a cellular, biochemical and molecular analysis of the degeneration process that occurs in the larval midgut of Bombyx mori during larval-adult transformation, with the aim to analyze autophagy and apoptosis in cells that die under physiological conditions. We demonstrate that larval midgut degradation is due to the concerted action of the two mechanisms, which occur at different times and have different functions. Autophagy is activated from the wandering stage and reaches a high level of activity during the spinning and prepupal stages, as demonstrated by specific autophagic markers. Our data show that the process of autophagy can recycle molecules from the degenerating cells and supply nutrients to the animal during the non-feeding period. Apoptosis intervenes later. In fact, although genes encoding caspases are transcribed at the end of the larval period, the activity of these proteases is not appreciable until the second day of spinning and apoptotic features are observable from prepupal phase. The abundance of apoptotic features during the pupal phase, when the majority of the cells die, indicates that apoptosis is actually responsible for cell death and for the disappearance of larval midgut cells.

Magnetic resonance (MR) imaging is a widely used medical imaging protocol that can be configured to provide different contrasts between the tissues in human body. By setting different scanning parameters, each MR imaging modality reflects the unique visual characteristic of scanned body part, benefiting the subsequent analysis from multiple perspectives. To utilize the complementary information from multiple imaging modalities, cross-modality MR image synthesis has aroused increasing research interest recently. However, most existing methods only focus on minimizing pixel/voxel-wise intensity difference but ignore the textural details of image content structure, which affects the quality of synthesized images. In this paper, we propose edge-aware generative adversarial networks (Ea-GANs) for cross-modality MR image synthesis. Specifically, we integrate edge information, which reflects the textural structure of image content and depicts the boundaries of different objects in images, to reduce this gap. Corresponding to different learning strategies, two frameworks are proposed, i.e., a generator-induced Ea-GAN (gEa-GAN) and a discriminator-induced Ea-GAN (dEa-GAN). The gEa-GAN incorporates the edge information via its generator, while the dEa-GAN further does this from both the generator and the discriminator so that the edge similarity is also adversarially learned. In addition, the proposed Ea-GANs are 3D-based and utilize hierarchical features to capture contextual information. The experimental results demonstrate that the proposed Ea-GANs, especially the dEa-GAN, outperform multiple state-of-the-art methods for cross-modality MR image synthesis in both qualitative and quantitative measures. Moreover, the dEa-GAN also shows excellent generality to generic image synthesis tasks on benchmark datasets about facades, maps, and cityscapes.

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