Sagittal balance parameters measurement on cervical spine MR images based on superpixel segmentation

Abstract

Introduction: 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. Method: 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. Result: 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. Discussion: 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.

Keywords: artificial intelligence; cervical spine; magnetic resonance imaging; sagittal balance parameters; superpixel segmentation.

FIGURE 1

The process of image preprocessing on the original image. To enhance the overall contrast of the original images (A), bilateral filtering (B) and CLAHE equalization (C) are used in the preprocessing.

FIGURE 2

The proposed algorithm flow of cervical curvature measurement based on superpixel segmentation.

FIGURE 3

Different superpixel segmentation performance under different number of superpixels.

FIGURE 4

Schematic diagram of cervical curvature measured by CCL methods. (A) Modified CCL method was used on the cervical spine (CCL-A and CCL-B). (B) The acquisition of the centroid for vertebra and the calculation of curvature in the cervical MRI image.

FIGURE 5

Schematic diagram of C7 slope measured by the minimum bounding rectangle fitting methods.

FIGURE 6

Comparison between SLIC, Vbseg and PDB-SLIC algorithm for visual perception of vertebral body segmentation.

FIGURE 7

Vertebrae segmentation performance of precision, recall, and Jaccard of SLIC, Vbseg and PDB-SLIC algorithm under different superpixel numbers. (A) Jaccard; (B) Precision; (C) Recall.

FIGURE 8

Cervical curvature measurements at different ages group.

FIGURE 9

Diagram of trends in cervical curvature across different hospitals, MRI facilities, sex in different age groups. (A) Different hospitals; (B) Different MRI facilities; (C) Different sex.

FIGURE 10

Heatmap of correlation analysis results for three sagittal cervical balance parameters measured by PDB-SLIC.