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曹正文, 乔念祖, 卜起荣, 冯筠. 结合超像素和U型全卷积网络的胰腺分割方法[J]. 计算机辅助设计与图形学学报, 2019, 31(10): 1777-1785. DOI: 10.3724/SP.J.1089.2019.17655
引用本文: 曹正文, 乔念祖, 卜起荣, 冯筠. 结合超像素和U型全卷积网络的胰腺分割方法[J]. 计算机辅助设计与图形学学报, 2019, 31(10): 1777-1785. DOI: 10.3724/SP.J.1089.2019.17655
Cao Zhengwen, Qiao Nianzu, Bu Qirong, Feng Jun. Superpixel Combining U-NET for Pancreas Segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(10): 1777-1785. DOI: 10.3724/SP.J.1089.2019.17655
Citation: Cao Zhengwen, Qiao Nianzu, Bu Qirong, Feng Jun. Superpixel Combining U-NET for Pancreas Segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(10): 1777-1785. DOI: 10.3724/SP.J.1089.2019.17655

结合超像素和U型全卷积网络的胰腺分割方法

Superpixel Combining U-NET for Pancreas Segmentation

  • 摘要: 为了提高现有胰腺图像分割方法性能,提出一种超像素和U型全卷积网络(U-NET)结合的胰腺图像分割方法.首先,提出一种胰腺CT图像的超像素分割方法;然后,依据分割结果对图像进行映射降维得到腹部视觉概要图像,再将其与超像素位置信息作为U型全卷积网络的输入;最后,得到分割好的胰腺器官.在NIH胰腺公开数据集上的实验结果表明,文中方法将戴斯相似系数(DSC)提高到87.9%,高于目前已有的胰腺图像分割方法.并且其运算速度高于U-NET.

     

    Abstract: In order to improve the performance of pancreas segmentation, this paper proposes a pancreas segmentation method combined superpixel and U-NET. Firstly, we propose a medical superpixel segmentation method. Then we map and reduce dimensionality to obtain visual summary image according to the result of superpixel segmentation. Finally, we use the visual summary image and superpixel position information as the input of U-NET to obtain the pancreas segmentation result. The experimental results on the NIH pancreas public dataset show that the DSC of this method is 87.9%, which is higher than all current pancreas segmentation methods;and the method is faster than U-NET.

     

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