SSCK-Net: Spine segmentation in MRI based on cross attention and key-points recognition-assisted learner

Abstract

Multiple spine segmentation of vertebrae and intervertebral discs for magnetic resonance images (MRI) plays a significant role in various spinal diseases diagnosis and treatments of spine disorder. However, the inherent complexity and characteristics of the spine pose challenges in balancing the inter-class similarity and intra-class variety of spine, improving generalization ability, learning rate, and accuracy. To address the above issues, a spine segmentation in MRI based on cross attention and key-points recognition-assisted learner (SSCK-Net) is proposed. Firstly, a multi-channel cross attention (MCCA) mechanism is put forward to generate a comprehensive description of the spine by fusing complementary inter-class feature and intra-class feature. Secondly, a key-points recognition-assisted learner (KRAL) is designed, which includes mixed-supervision recognition-assisted label fusion (RALF), to reduce dependence on a single dataset and improve the generalization ability of the network. Subsequently, adaptive transformer (AT) is presented to perform parallel computation through selective kernel fusion, nonlinear activation function with simple inverse and rescaling layer normalization, which reduces training time and prevents performance degradation due to long-term dependence. Finally, a feature enhances unit (FEU) is proposed to fuse the key point features and the segmented features while constraining the key-point features to the fused features again by the refine unit. Our experiments on T2-weighted volumetric MRI show that SSCK-Net achieves impressive performance with a mean Dice similarity coefficient (DSC) 96.12% for 5 vertebral bodies and 95.07% for 5 intervertebral discs on public datasets, outperforming state-of-the-art techniques.