基于深度学习的脊椎CT图像分割

Vertebra CT image segmentation based on deep learning

脊椎CT图像分割是脊椎三维重建可视化的关键。针对脊椎CT图像中脊椎边缘模糊,结构复杂,形状多变等问题,基于深度学习方法提出一种双解码器网络。该网络在编码解码网络U-Net结构基础上增加了一条结构相同的并行解码分支,两个解码分支可以互补地提取图像特征。并且,在编码与解码之间加入双重特征融合模块,解决网络在下采样和上采样过程中造成的语义信息丢失问题。同时用密连混合卷积模块代替原始卷积模块,提高网络对多尺度特征的提取能力。此外加入高效注意力模块,使网络在空间上注重学习感兴趣区域,在通道上抑制无关信息。在CSI2014公开数据集上进行测试,Dice系数达到0.970,Jaccard系数达到0.945,召回率达到0.962。实验结果表明,该网络能够提高脊椎分割精度,具有较好的泛化能力,可以满足临床脊椎CT图像分割需求。

Vertebra CT image segmentation is the key to the visualization of vertebra 3 D reconstruction. Aiming at the problems of blurred vertebra edge, complex structure and changeable shape in vertebra CT images, a dual-decoder network is proposed based on deep learning method. The network adds a parallel decoding branch with the same structure on the basis of the U-Net structure of the encoding and decoding network, and the two decoding branches can extract image features complementary. Moreover, a dual feature fusion module is added between encoding and decoding to solve the problem of semantic information loss caused by the network downsampling and upsampling. At the same time, the original convolution module is replaced by the densely connected hybrid convolution module to improve the network′s ability to extract multi-scale features. In addition, an efficient attention module is added to make the network focus on learning regions of interest in space and suppress irrelevant information in channels. Tested on the CSI2014 public dataset, the Dice coefficient reaches 0.970, the Jaccard coefficient reaches 0.945, and the Recall rate reaches 0.962. The experimental results show that the network can improve the accuracy of vertebra segmentation, has better generalization ability, and can meet the needs of clinical vertebra CT image segmentation.