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您的检索式：作者名="Feihang Wang"
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| 1 | Application of contrast-enhanced ultrasound in minimally invasive ablation of benign thyroid nodules显示文摘Objective:This study aimed to investigate the application value of contrast-enhanced ultrasound(CEUS)before and after minimally invasive ablation procedures for benign thyroid nodule(s)(BTN).Methods:This prospective study included patients with BTNs scheduled to undergo ultrasound-guided minimally invasive ablation treatment.CEUS was performed before and after ablation(at 1 day,and 1,6,and 12 months after ablation).Changes in microvascular perfusion and the volume of BTNs were noted and assessed.Results:Sixty-two patients(62 BTNs),who underwent ablation procedures between June 2016 and August 2020,were included.All lesions were confirmed by biopsy,and histopathological results were obtained before ablation treatment.On preoperative CEUS,the lesions exhibited hyperenhancement(53.23%)or iso-enhancement(46.77%)during the arterial phase,and all lesions exhibited iso-enhancement in the venous and late phases.One day after ablation,none of the BTNs exhibited obvious enhancement on CEUS.One(1.61%)lesion was retreated due to a nodule-like enhancement area detected by CEUS at the 6-month follow-up.The mean nodular volume reduction rate(VRR)at 1,6,and 12 months follow-up demonstrated no significant difference between the two ablation groups(microwave ablation versus radiofrequency ablation).Twelve months after ablation,the mean(±SD)VRR of all BTNs was 60.3±10.3%.Conclusion:CEUS helped guide treatment decisions for BTNs before ablation treatment.Moreover,it could also be used to accurately and noninvasively evaluate treatment efficacy. | Jiaying Cao Peili Fan Feihang Wang Shuainan Shi Lingxiao Liu Zhiping Yan Yi Dong Wenping Wang | 2022 | Journal of Interventional Medicine2022，5，1： | 3 |
| 2 | Improving the Interpretability and Reliability of Regional Land Cover Classification by U-Net Using Remote Sensing Data显示文摘The accurate and reliable interpretation of regional land cover data is very important for natural resource monitoring and environmental assessment.At present,refined land cover data are mainly obtained by manual visual interpretation,which has the problems of heavy workload and inconsistent interpretation scales.Deep learning has greatly improved the automatic processing and analysis of remote sensing data.However,the accurate interpretation of feature information from massive datasets remains a difficult problem in wide regional land cover classification.To improve the efficiency of deep learning-based remote sensing image interpretation,we selected multisource remote sensing data,assessed the interpretability of the U-Net model based on surface spatial scenes with different levels of complexity,and proposed a new method of stereoscopic accuracy verification(SAV)to evaluate the reliability of the classification result.The results show that classification accuracy is more highly correlated with terrain and landscape than with other factors related to image data,such as platform and spatial resolution.As the complexity of surface spatial scenes increases,the accuracy of the classification results mainly shows a fluctuating declining trend.We also find the distribution characteristics from the SAV evaluation results of different land cover types in each surface spatial scene.Based on the results observed in this study,we consider the distinction of interpretability and reliability in diverse ground object types and design targeted classification strategies for different surface scenes,which can greatly improve the classification efficiency.The key achievement of this study is to provide the theoretical basis for remote sensing information analysis and an accuracy evaluation method for regional land cover classification,and the proposed method can help improve the likelihood that intelligent interpretation can replace manual acquisition. | WANG Xinshuang CAO Jiancheng LIU Jiange LI Xiangwu WANG Lu ZUO Feihang BAI Mu | 2022 | Chinese Geographical Science2022，32，6： | 0 |
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