Zhong Yi-Fan, Dai Yu-Xiang, Li Shi-Pian, Zhu Ke-Jia, Lin Yong-Peng, Ran Yu, Chen Lin, Ruan Ye, Yu Peng-Fei, Li Lin, Li Wen-Xiong, Xu Chuang-Long, Sun Zhi-Tao, Weber Kenneth A, Kong De-Wei, Yang Feng, Lin Wen-Ping, Chen Jiang, Chen Bo-Lai, Jiang Hong, Zhou Ying-Jie, Sheng Bo, Wang Yong-Jun, Tian Ying-Zhong, Sun Yue-Li
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai, China.
Front Bioeng Biotechnol. 2024 Apr 12;12:1337808. doi: 10.3389/fbioe.2024.1337808. eCollection 2024.
Magnetic Resonance Imaging (MRI) is essential in diagnosing cervical spondylosis, providing detailed visualization of osseous and soft tissue structures in the cervical spine. However, manual measurements hinder the assessment of cervical spine sagittal balance, leading to time-consuming and error-prone processes. This study presents the Pyramid DBSCAN Simple Linear Iterative Cluster (PDB-SLIC), an automated segmentation algorithm for vertebral bodies in T2-weighted MR images, aiming to streamline sagittal balance assessment for spinal surgeons. PDB-SLIC combines the SLIC superpixel segmentation algorithm with DBSCAN clustering and underwent rigorous testing using an extensive dataset of T2-weighted mid-sagittal MR images from 4,258 patients across ten hospitals in China. The efficacy of PDB-SLIC was compared against other algorithms and networks in terms of superpixel segmentation quality and vertebral body segmentation accuracy. Validation included a comparative analysis of manual and automated measurements of cervical sagittal parameters and scrutiny of PDB-SLIC's measurement stability across diverse hospital settings and MR scanning machines. PDB-SLIC outperforms other algorithms in vertebral body segmentation quality, with high accuracy, recall, and Jaccard index. Minimal error deviation was observed compared to manual measurements, with correlation coefficients exceeding 95%. PDB-SLIC demonstrated commendable performance in processing cervical spine T2-weighted MR images from various hospital settings, MRI machines, and patient demographics. The PDB-SLIC algorithm emerges as an accurate, objective, and efficient tool for evaluating cervical spine sagittal balance, providing valuable assistance to spinal surgeons in preoperative assessment, surgical strategy formulation, and prognostic inference. Additionally, it facilitates comprehensive measurement of sagittal balance parameters across diverse patient cohorts, contributing to the establishment of normative standards for cervical spine MR imaging.
磁共振成像（MRI）对于诊断颈椎病至关重要，它能提供颈椎骨和软组织结构的详细可视化图像。然而，手动测量阻碍了颈椎矢状面平衡的评估，导致过程既耗时又容易出错。本研究提出了金字塔密度空间聚类算法（Pyramid DBSCAN）简单线性迭代聚类（PDB - SLIC），这是一种用于T2加权磁共振图像中椎体的自动分割算法，旨在简化脊柱外科医生对矢状面平衡的评估。PDB - SLIC将SLIC超像素分割算法与密度空间聚类算法（DBSCAN）相结合，并使用来自中国十家医院的4258例患者的大量T2加权正中矢状面磁共振图像数据集进行了严格测试。在超像素分割质量和椎体分割准确性方面，将PDB - SLIC的有效性与其他算法和网络进行了比较。验证包括对颈椎矢状参数的手动和自动测量的对比分析，以及对PDB - SLIC在不同医院环境和磁共振扫描机器上测量稳定性的审查。PDB - SLIC在椎体分割质量方面优于其他算法，具有高精度、召回率和杰卡德指数。与手动测量相比，误差偏差最小，相关系数超过95%。PDB - SLIC在处理来自不同医院环境、磁共振成像机器和患者人口统计学特征的颈椎T2加权磁共振图像时表现出了出色的性能。PDB - SLIC算法成为评估颈椎矢状面平衡的准确、客观和高效工具，为脊柱外科医生在术前评估、手术策略制定和预后推断方面提供了有价值的帮助。此外，它有助于对不同患者群体的矢状面平衡参数进行全面测量，有助于建立颈椎磁共振成像的规范标准。
