Li Chaofan, Chen Liping, Liu Qiong, Teng Jinnan
Yancheng Third People's Hospital, Affiliated Hospital 6 of Nantong University, The Affiliated Hospital of Jiangsu Medical College, Yancheng, 224001, Jiangsu, China.
School of Medical Imaging, Jiangsu Medical College, Yancheng, 224005, Jiangsu, China.
Sci Rep. 2025 Aug 1;15(1):28131. doi: 10.1038/s41598-025-13569-0.
The lumbar spine and pelvis are critical stress-bearing structures of the human body, and their rapid and accurate segmentation plays a vital role in clinical diagnosis and intervention. However, conventional CT imaging poses significant challenges due to the low contrast of sacral and bilateral hip tissues and the complex and highly similar intervertebral space structures within the lumbar spine. To address these challenges, we propose a general-purpose segmentation network that integrates a cross-scale feature fusion strategy with a linear self-attention mechanism. The proposed network effectively extracts multi-scale features and fuses them along the channel dimension, enabling both structural and boundary information of lumbar and pelvic regions to be captured within the encoder-decoder architecture.Furthermore, we introduce a linear mapping strategy to approximate the traditional attention matrix with a low-rank representation, allowing the linear attention mechanism to significantly reduce computational complexity while maintaining segmentation accuracy for vertebrae and pelvic bones. Comparative and ablation experiments conducted on the CTSpine1K and CTPelvic1K datasets demonstrate that our method achieves improvements of 1.5% in Dice Similarity Coefficient (DSC) and 2.6% in Hausdorff Distance (HD) over state-of-the-art models, validating the effectiveness of our approach in enhancing boundary segmentation quality and segmentation accuracy in homogeneous anatomical regions.
腰椎和骨盆是人体重要的承重结构，对其进行快速准确的分割在临床诊断和干预中起着至关重要的作用。然而，由于骶骨和双侧髋部组织的对比度低以及腰椎内复杂且高度相似的椎间隙结构，传统的CT成像带来了重大挑战。为应对这些挑战，我们提出了一种通用分割网络，该网络将跨尺度特征融合策略与线性自注意力机制相结合。所提出的网络有效地提取多尺度特征并沿通道维度进行融合，使得在编码器-解码器架构中能够捕捉腰椎和骨盆区域的结构和边界信息。此外，我们引入一种线性映射策略，用低秩表示近似传统注意力矩阵，使线性注意力机制在保持椎骨和骨盆骨分割精度的同时显著降低计算复杂度。在CTSpine1K和CTPelvic1K数据集上进行的对比和消融实验表明，我们的方法在骰子相似系数（DSC）上比现有模型提高了1.5%，在豪斯多夫距离（HD）上提高了2.6%，验证了我们的方法在提高均匀解剖区域边界分割质量和分割精度方面的有效性。
