Beijing Institute of Technology, School of Mechanical Engineering, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Beijing Institute of Technology, School of Mechanical Engineering, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
Artif Intell Med. 2022 Feb;124:102243. doi: 10.1016/j.artmed.2022.102243. Epub 2022 Jan 8.
An axial MRI image of the lumbar spine generally contains multiple spinal structures and their simultaneous segmentation will help analyze the pathogenesis of the spinal disease, generate the spinal medical report, and make a clinical surgery plan for the treatment of the spinal disease. However, it is still a challenging issue that multiple spinal structures are segmented simultaneously and accurately because of the large diversities of the same spinal structure in intensity, resolution, position, shape, and size, the implicit borders between different structures, and the overfitting problem caused by the insufficient training data. In this paper, we propose a novel network framework ResAttenGAN to address these challenges and achieve the simultaneous and accurate segmentation of disc, neural foramina, thecal sac, and posterior arch. ResAttenGAN comprises three modules, i.e. full feature fusion (FFF) module, residual refinement attention (RRA) module, and adversarial learning (AL) module. The FFF module captures multi-scale feature information and fully fuse the features at all hierarchies for generating the discriminative feature representation. The RRA module is made up of a local position attention block and a residual border refinement block to accurately locate the implicit borders and refine their pixel-wise classification. The AL module smooths and strengthens the higher-order spatial consistency to solve the overfitting problem. Experimental results show that the three integrated modules in ResAttenGAN have advantages in tackling the above challenges and ResAttenGAN outperforms the existing segmentation methods under evaluation metrics.
腰椎轴向 MRI 图像通常包含多个脊柱结构，同时对这些结构进行分割有助于分析脊柱疾病的发病机制，生成脊柱医学报告，并为脊柱疾病的治疗制定临床手术计划。然而，由于同一脊柱结构在强度、分辨率、位置、形状和大小、不同结构之间的隐含边界以及由于训练数据不足导致的过拟合问题，同时准确地对多个脊柱结构进行分割仍然是一个具有挑战性的问题。在本文中，我们提出了一种新的网络框架 ResAttenGAN 来解决这些挑战，并实现椎间盘、神经孔、脊膜囊和后弓的同时准确分割。ResAttenGAN 包括三个模块，即全特征融合（FFF）模块、残差细化注意（RRA）模块和对抗学习（AL）模块。FFF 模块捕获多尺度特征信息，并充分融合所有层次的特征，以生成有区别的特征表示。RRA 模块由局部位置注意块和残差边界细化块组成，以准确定位隐含边界并细化其像素级分类。AL 模块平滑并加强了更高阶的空间一致性，以解决过拟合问题。实验结果表明，ResAttenGAN 中的三个集成模块在解决上述挑战方面具有优势，在评估指标下优于现有的分割方法。
