Xue Zhilong, Deng Shuangcheng, Yue Yiqun, Chen Chenping, Li Zhiwu, Yang Yang, Sun Shilong, Liu Yubang
Beijing Institute of Petrochemical Technology, Qingyuan North Road, No. 19, Daxing District, Beijing 102617, China, Beijing, 102617, CHINA.
Beijing Institute of Petrochemical Technology, Qingyuan North Road, No. 19, Daxing District, Beijing 102617, China, Beijing, Beijing, 102617, CHINA.
Biomed Phys Eng Express. 2025 Aug 21. doi: 10.1088/2057-1976/adfde9.
In recent years, spinal X-ray image segmentation has played a vital role in the computer-aided diagnosis of various adolescent spinal disorders. However, due to the complex morphology of lesions and the fact that most existing methods are tailored to single-disease scenarios, current segmentation networks struggle to balance local detail preservation and global structural understanding across different disease types. As a result, they often suffer from limited accuracy, insufficient robustness, and poor adaptability. To address these challenges, we propose a novel fully automated spinal segmentation network, DCE-UNet, which integrates the local modeling strength of convolutional neural networks (CNNs) with the global contextual awareness of Transformers. The network introduces several architectural and feature fusion innovations. Specifically, a lightweight Transformer module is incorporated in the encoder to model high-level semantic features and enhance global contextual understanding. In the decoder, a Rec-Block module combining residual convolution and channel attention is designed to improve feature reconstruction and multi-scale fusion during the upsampling process. Additionally, the downsampling feature extraction path integrates a novel DC-Block that fuses channel and spatial attention mechanisms, enhancing the network's ability to represent complex lesion structures. Experiments conducted on a self-constructed large-scale multi-disease adolescent spinal X-ray dataset demonstrate that DCE-UNet achieves a Dice score of 91.3%, a mean Intersection over Union (mIoU) of 84.1, and a Hausdorff Distance (HD) of 4.007, outperforming several state-of-the-art comparison networks. Validation on real segmentation tasks further confirms that DCE-UNet delivers consistently superior performance across various lesion regions, highlighting its strong adaptability to multiple pathologies and promising potential for clinical application.
近年来，脊柱X线图像分割在各种青少年脊柱疾病的计算机辅助诊断中发挥了至关重要的作用。然而，由于病变形态复杂，且大多数现有方法是针对单一疾病场景定制的，当前的分割网络难以在不同疾病类型之间平衡局部细节保留和全局结构理解。因此，它们常常存在准确性有限、鲁棒性不足和适应性差的问题。为应对这些挑战，我们提出了一种新型的全自动脊柱分割网络DCE-UNet，它将卷积神经网络（CNN）的局部建模能力与Transformer的全局上下文感知能力相结合。该网络引入了多项架构和特征融合创新。具体而言，在编码器中融入了一个轻量级Transformer模块，以对高级语义特征进行建模并增强全局上下文理解。在解码器中，设计了一个结合残差卷积和通道注意力的Rec-Block模块，以改善上采样过程中的特征重建和多尺度融合。此外，下采样特征提取路径集成了一个融合通道和空间注意力机制的新型DC-Block，增强了网络表示复杂病变结构的能力。在自行构建的大规模多疾病青少年脊柱X线数据集上进行的实验表明，DCE-UNet的Dice分数达到91.3%，平均交并比（mIoU）为84.1，豪斯多夫距离（HD）为4.007，优于多个先进的对比网络。在实际分割任务上的验证进一步证实，DCE-UNet在各个病变区域均表现出始终如一的卓越性能，凸显了其对多种病理状况的强大适应性以及在临床应用中的广阔前景。
