Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China;
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China;
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China;
Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China;
Image segmentation; Diseases; Biomedical imaging; Deep learning; Training; Testing; White matter;
机译:适用于白质超萎缩(WMHS)分割的全自动工具的体积精度
机译:多中心数据集中五种自动白品超强度分段方法的性能
机译:在阿尔茨海默氏病风险和衰老研究中使用监督性分割方法提取和总结白质高信号
机译:一种改善白质超强度分割精度的后处理方法,用于随机初始化U-Net
机译:淀粉样蛋白沉积和FDG吸收在衰老和认知性受损的患者中的关联,具有使用简单的机器学习技术与严重脑膜下白物质过度收缩的竞争者
机译:使用不规则年龄图的白色物质高强度分割的扩张显着性U-Net
机译:Triplanar集合U-NET模型用于MR图像的白质超萎缩分段