School of Automation and Mechanical Engineering, Shanghai University, Shanghai, 200072, China.
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai, 200072, China.
Nat Commun. 2022 Feb 11;13(1):841. doi: 10.1038/s41467-022-28387-5.
To help doctors and patients evaluate lumbar intervertebral disc degeneration (IVDD) accurately and efficiently, we propose a segmentation network and a quantitation method for IVDD from T2MRI. A semantic segmentation network (BianqueNet) composed of three innovative modules achieves high-precision segmentation of IVDD-related regions. A quantitative method is used to calculate the signal intensity and geometric features of IVDD. Manual measurements have excellent agreement with automatic calculations, but the latter have better repeatability and efficiency. We investigate the relationship between IVDD parameters and demographic information (age, gender, position and IVDD grade) in a large population. Considering these parameters present strong correlation with IVDD grade, we establish a quantitative criterion for IVDD. This fully automated quantitation system for IVDD may provide more precise information for clinical practice, clinical trials, and mechanism investigation. It also would increase the number of patients that can be monitored.
为了帮助医生和患者准确、高效地评估腰椎间盘退变（IVDD），我们提出了一种基于 T2MRI 的 IVDD 分割网络和定量方法。一个由三个创新模块组成的语义分割网络（BianqueNet）实现了 IVDD 相关区域的高精度分割。一种定量方法用于计算 IVDD 的信号强度和几何特征。手动测量与自动计算具有极好的一致性，但后者具有更好的可重复性和效率。我们在大量人群中研究了 IVDD 参数与人口统计学信息（年龄、性别、位置和 IVDD 分级）之间的关系。考虑到这些参数与 IVDD 分级有很强的相关性，我们建立了 IVDD 的定量标准。这种用于 IVDD 的全自动定量系统可以为临床实践、临床试验和机制研究提供更精确的信息。它还可以增加可以监测的患者数量。
