Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and su...Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.展开更多
目的通过Meta分析评价主动呼吸循环技术(ACBT)改善肺癌患者术后的临床效果。方法检索从建库至2024年2月在中国知网、万方、维普、PubMed、Embase、the Cochrane Library、Web of Science等数据库中发表的有关ACBT改善肺癌患者术后情况...目的通过Meta分析评价主动呼吸循环技术(ACBT)改善肺癌患者术后的临床效果。方法检索从建库至2024年2月在中国知网、万方、维普、PubMed、Embase、the Cochrane Library、Web of Science等数据库中发表的有关ACBT改善肺癌患者术后情况的随机对照试验(RCT)。采用RevMan5.3软件进行Meta分析。结果共纳入10篇文献,1044例患者。Meta分析结果显示,试验组的第1秒用力呼气容积(FEV1)高于对照组,差异具有统计学意义(P<0.05)。试验组的6 min步行距离(6MWT)长于对照组,差异具有统计学意义(P<0.05)。试验组的术后住院时间短于对照组,差异具有统计学意义(P<0.05)。试验组的肺不张发生率低于对照组,差异具有统计学意义(P<0.05)。结论ACBT可以改善肺癌患者术后肺功能,增强运动能力,减少肺部并发症的发生,缩短术后住院时间。展开更多
文摘Quantitative analysis of digital images requires detection and segmentation of the borders of the object of interest. Accurate segmentation is required for volume determination, 3D rendering, radiation therapy, and surgery planning. In medical images, segmentation has traditionally been done by human experts. Substantial computational and storage requirements become especially acute when object orientation and scale have to be considered. Therefore, automated or semi-automated segmentation techniques are essential if these software applications are ever to gain widespread clinical use. Many methods have been proposed to detect and segment 2D shapes, most of which involve template matching. Advanced segmentation techniques called Snakes or active contours have been used, considering deformable models or templates. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.
