Segmentation of Spine Computed Tomography Images Based on Three-Dimensional Recurrent Residual Convolution
The automatic segmentation of spine Computed Tomography(CT)images can assist doctors in diagnosing related diseases.Compared to Three-Dimensional(3D)reconstruction after Two-Dimensional(2D)segmentation,the 3D segmentation method is more convenient and can retain the spatial information of the image.To address the problem of the low accuracy of 3D spine segmentation,a U-Net based on 3D recurrent residual convolution to segment CT images of the spine is proposed in this study.A coordination attention mechanism is introduced in the network front to focus the network on the region of interest.A 3D recurrent residual module is used instead of a typical convolution module to accumulate features effectively and mitigate gradient disappearance.An efficient connected hybrid convolution module is added to preserve the tiny features.The dual-feature residual attention module is used instead of the jump connection for multiscale fusion to fuse semantics between high and low levels,and the global context is modeled by aggregating the features of different levels to improve the segmentation performance.First,the model is tested on the public datasets of CSI2014,and compared with other 3D segmentation networks and different spine segmentation methods,the Dice Similarity Coefficient(DSC)reaches 93.85％,which is 1.77-7.65 percentage points higher than those of other six segmentation networks and 1.67-10.85 percentage points higher than those of other spine segmentation methods.The model is also tested on the local lumbar dataset,and the DSC is increased by 1.51-19.86 percentage points compared with those of the other six segmentation models,verifying the effectiveness of the method proposed in this study and the feasibility of applying it to computer-aided diagnosis and treatment.
万方数据
维普
