Radiologist-Validated Automatic Lumbar T1-Weighted Spinal MRI Segmentation Tool via an Attention U-Net Algorithm

Background/Objectives: Spinal MRI segmentation has become increasingly important with the prevalence of disc herniation and vertebral injuries. Artificial intelligence can help orthopedic surgeons and radiologists automate the process of segmentation. Currently, there are few tools for T1-weighted spinal MRI segmentation, with most focusing on T2-weighted imaging. This paper focuses on creating an automatic lumbar spinal MRI segmentation tool for T1-weighted images using deep learning. Methods: An Attention U-Net was employed as the main algorithm because the architecture has shown success in other segmentation applications. Segmentation loss functions were compared, focusing on the difference between BCE and MSE loss. Two board-certified radiologists scored the output of the Attention U-Net versus four other algorithms to assess clinical relevance and segmentation accuracy. Results: The Attention U-Net achieved superior results, with SSIM and DICE coefficients of 0.998 and 0.93, outperforming other architectures. Both radiologists agreed that the Attention U-Net segmented lumbar spinal images with the highest accuracy on the Likert Scale (3.7 ± 0.82). Cohen's Kappa coefficient was measured at 0.31, indicating a fair level of agreement. MSE loss outperformed BCE with respect to both SSIM and DICE, serving as the loss function of choice. Conclusions: Qualitative observations showed that the Attention U-Net and U-Net++ were the top performing networks. However, the Attention U-Net minimized external noise and focused on internal spinal preservation, demonstrating strong segmentation performance for T1-weighted lumbar spinal MRI.