脊柱作为人体支撑的核心要素,对维护身体健康和功能至关重要,手术机器人的出现为复杂脊柱手术提供了一种新的解决方案。但是2D X射线图像只能在有限的投影视图中为机器人系统提供重叠的解剖信息,这意味着它无法直观地显示完整的全视图...脊柱作为人体支撑的核心要素,对维护身体健康和功能至关重要,手术机器人的出现为复杂脊柱手术提供了一种新的解决方案。但是2D X射线图像只能在有限的投影视图中为机器人系统提供重叠的解剖信息,这意味着它无法直观地显示完整的全视图解剖信息和精确的立体结构,而提供3D图像的术中CT扫描技术又增加了患者和医护人员受到辐射暴露的风险。为了在不增加时间成本且最大程度减少辐射的前提下,能够实时为手术机器人导航和准确定位提供更全面的解剖结构,提出一种用于脊柱X射线重建CT图像的V形卷积注意力网络。所提出的网络通过编码器和解码器之间的任务一致性以减小特征映射之间的语义差异,同时利用通道注意力机制来迫使网络关注重要特征区域,可有效减小冗余特征信息,从而提高网络训练效率。实验得出CT图像中脊柱结构的结构相似性指数(structural similarity index,SSIM)值为0.786,峰值信噪比(peak signal to noise ratio,PSNR)值为34.60 dB,证明通过X射线图像进行精准的3D重建为手术机器人提供图像支持拥有巨大潜力。展开更多
Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address t...Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.展开更多
文摘脊柱作为人体支撑的核心要素,对维护身体健康和功能至关重要,手术机器人的出现为复杂脊柱手术提供了一种新的解决方案。但是2D X射线图像只能在有限的投影视图中为机器人系统提供重叠的解剖信息,这意味着它无法直观地显示完整的全视图解剖信息和精确的立体结构,而提供3D图像的术中CT扫描技术又增加了患者和医护人员受到辐射暴露的风险。为了在不增加时间成本且最大程度减少辐射的前提下,能够实时为手术机器人导航和准确定位提供更全面的解剖结构,提出一种用于脊柱X射线重建CT图像的V形卷积注意力网络。所提出的网络通过编码器和解码器之间的任务一致性以减小特征映射之间的语义差异,同时利用通道注意力机制来迫使网络关注重要特征区域,可有效减小冗余特征信息,从而提高网络训练效率。实验得出CT图像中脊柱结构的结构相似性指数(structural similarity index,SSIM)值为0.786,峰值信噪比(peak signal to noise ratio,PSNR)值为34.60 dB,证明通过X射线图像进行精准的3D重建为手术机器人提供图像支持拥有巨大潜力。
基金the Project of China Scholarship Council(No.201708615011)the Xi’an Science and Technology Plan Project(No.GXYD1.7)。
文摘Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.
