A dual channel and spatial attention network for automatic spine segmentation of MRI images

Accurate image segmentation plays an essential role in diagnosing and treating various spinal diseases. However, traditional segmentation methods often consume a lot of time and energy. This research proposes an innovative deep-learning-based automatic segmentation method for spine magnetic resonance imaging (MRI) images. The proposed method DAUNet++ is supported by UNet++, which adds residual structure and attention mechanism. Specifically, a residual block is utilized for down-sampling to construct the RVNet, as a new skeleton structure. Furthermore, two novel types of dual channel and spatial attention modules are proposed to emphasize rich feature regions, enhance useful information, and improve the network performance by recalibrating the characteristic. The published spinesagt2wdataset3 spinal MRI image dataset is adopted in the experiment. The dice similarity coefficient score on the test set is 0.9064. Higher segmentation accuracy and efficiency are achieved, indicating the effectiveness of the proposed method.