Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis.

Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical diagnosis. The training of new sonographers and deep learning based algorithms for US image analysis usually requires a large amount of data. However, obtaining and labeling large-scale US imaging data are not easy tasks, especially for diseases with low incidence. Realistic US image synthesis can alleviate this problem to a great extent. In this paper, we propose a generative adversarial network (GAN) based image synthesis framework. Our main contributions include: (1) we present the first work that can synthesize realistic B-mode US images with high-resolution and customized texture editing features; (2) to enhance structural details of generated images, we propose to introduce auxiliary sketch guidance into a conditional GAN. We superpose the edge sketch onto the object mask and use the composite mask as the network input; (3) to generate high-resolution US images, we adopt a progressive training strategy to gradually generate high-resolution images from low-resolution images. In addition, a feature loss is proposed to minimize the difference of high-level features between the generated and real images, which further improves the quality of generated images; (4) the proposed US image synthesis method is quite universal and can also be generalized to the US images of other anatomical structures besides the three ones tested in our study (lung, hip joint, and ovary); (5) extensive experiments on three large US image datasets are conducted to validate our method. Ablation studies, customized texture editing, user studies, and segmentation tests demonstrate promising results of our method in synthesizing realistic US images.

Rigorous Assessment of Guidelines on COVID-19-Related Thrombotic or Thromboembolic Disease: Implications for Clinical Practice of Prevention, Diagnosis, and Treatment.

PURPOSE: Severe COVID-19 patients were prone to develop venous thromboembolism. Unfortunately, to date, there is no evidence of any effective medications for thromboembolism in COVID-19. The management of the disease relies on symptomatic and supportive treatments, giving rise to a variety of guidelines. However, the quality of methodology and clinical recommendations remains unknown. MATERIALS AND METHODS: We searched Medline, Cochrane Library, Web of Science, websites of international organizations and medical societies, and gray literature databases. Four well-trained appraisers independently evaluated the quality of eligible guidelines and extracted recommendations using well-recognized guideline appraisal tools. Furthermore, recommendations were extracted and reclassified according to a composite grading system. RESULTS: The search identified 23 guidelines that offered 108 recommendations. Guidelines scored average on AGREE II criteria, with Scope and Purpose and Clarity of Presentation highest. Only five (22%) guidelines provided high-quality recommendations. The existed clinical recommendations were inconsistent in terms of prophylaxis, diagnosis, and treatment of thromboembolic disease to some extent. CONCLUSION: Current guidelines for COVID-19 thromboembolism are generally of low quality, and clinical recommendations on thromboembolism are principally supported by insufficient evidence. There is still an urgent need for more well-designed clinical trials as evidence to prevent adverse events and improve prognosis during COVID-19 treatment.