Automated Three-Dimensional Imaging and Pfirrmann Classification of Intervertebral Disc Using a Graphical Neural Network in Sagittal Magnetic Resonance Imaging of the Lumbar Spine

Abstract

This study aimed to develop a graph neural network (GNN) for automated three-dimensional (3D) magnetic resonance imaging (MRI) visualization and Pfirrmann grading of intervertebral discs (IVDs), and benchmark it against manual classifications. Lumbar IVD MRI data from 300 patients were retrospectively analyzed. Two clinicians assessed the manual segmentation and grading for inter-rater reliability using Cohen's kappa. The IVDs were then processed and classified using an automated convolutional neural network (CNN)-GNN pipeline, and their performance was evaluated using F1 scores. Manual Pfirrmann grading exhibited moderate agreement (κ = 0.455-0.565) among the clinicians, with higher exact match frequencies at lower lumbar levels. Single-grade discrepancies were prevalent except at L5/S1. Automated segmentation of IVDs using a pretrained U-Net model achieved an F1 score of 0.85, with a precision and recall of 0.83 and 0.88, respectively. Following 3D reconstruction of the automatically segmented IVD into a 3D point-cloud representation of the target intervertebral disc, the GNN model demonstrated moderate performance in Pfirrmann classification. The highest precision (0.81) and F1 score (0.71) were observed at L2/3, whereas the overall metrics indicated moderate performance (precision: 0.46, recall: 0.47, and F1 score: 0.46), with variability across spinal levels. The integration of CNN and GNN offers a new perspective for automating IVD analysis in MRI. Although the current performance highlights the need for further refinement, the moderate accuracy of the model, combined with its 3D visualization capabilities, establishes a promising foundation for more advanced grading systems.

Keywords: Artificial intelligence; Convolutional neural network; Graph neural network; Intervertebral disc degeneration; MRI; Machine learning; Pfirrmann classification; Spine imaging.

Fig. 1

a The figure shows example MRI images of the lumbar spine from a single patient, illustrating ground truth (top row, a) and U-Net segmentation masks (bottom row, b) of intervertebral discs. The segmentation metrics (IoU, F1, Precision, Recall) for each pair of images are as follows: 1 (0.74, 0.82, 0.87, 0.78), 2 (0.82, 0.89, 0.94, 0.85), 3 (0.91, 0.95, 0.95, 0.95), 4 (0.86, 0.92, 0.94, 0.90), 5 (0.91, 0.95, 0.96, 0.95), and 6 (0.96, 0.98, 0.97, 0.98). b The figure shows MRI images of the lumbar spine from a single patient, illustrating ground truth (top row, a) and U-Net segmentation masks (bottom row, b) of vertebra bodies. The segmentation metrics (IoU, F1, Precision, Recall) for each pair of images are as follows: 1 (0.78, 0.86, 0.87, 0.85), 2 (0.92, 0.96, 0.96, 0.95), 3 (0.91, 0.95, 0.92, 0.98), 4 (0.94, 0.97, 0.95, 0.99), 5 (0.93, 0.96, 0.97, 0.96), and 6 (0.89, 0.94, 0.91, 0.97)

Fig. 2

Overview of 3D reconstruction of the native IVD after automatic segmented MRI slices into 3D point cloud representation of a target intervertebral disc