N-Net: 3D Fully Convolution Network-Based Vertebrae Segmentation from CT Spinal Images

Accurate vertebrae segmentation from CT spinal images is crucial for the clinical tasks of diagnosis, surgical planning, and post-operative assessment. This paper describes an [Formula: see text]-shaped 3D fully convolution network (FCN) for vertebrae segmentation: [Formula: see text]-net. In this network, a global structure guidance pathway is designed for fusing the high-level semantic features with the global structure information. Moreover, the residual structure and the skip connection are introduced into traditional 3D FCN framework. These schemes can significantly improve the accuracy of vertebrae segmentation. Experimental results demonstrate the effectiveness and robustness of our method. A high average DICE score of 0.9499 [Formula: see text] 0.02 can be obtained, which is better than those of existing methods.