Deep learning for lumbar spine segmentation in magnetic resonance imaging—A systematic review

Lumbar spine disorders are a major cause of disability worldwide, often requiring magnetic resonance imaging (MRI) for accurate diagnosis and treatment planning. Segmentation of spinal structures in MRI is a critical yet time-consuming task that is traditionally performed manually by experts. In recent years, deep learning (DL) has emerged as a powerful tool to automate this process, offering improvements in efficiency, consistency, and accuracy. This systematic review presents a comprehensive analysis of DL-based methods for lumbar spine segmentation in MRI, conducted by searching the Scopus and PubMed databases for peer-reviewed journal articles and reviews. Studies were included only if they employed MRI as the imaging modality, applied deep learning techniques for segmentation, and explicitly reported both the model architecture and segmentation results. A total of 56 studies met these criteria, comprising 49 original research articles and 7 review articles. The selected works were analyzed in two main dimensions: (1) dataset characteristics, including image orientation, modality, annotation methods, and data availability; and (2) deep learning frameworks, covering preprocessing, data augmentation, network architectures, and evaluation metrics. Convolutional neural networks (CNNs), particularly U-Net and its variants, are the most used architectures, often enhanced with residual blocks, attention mechanisms, and multi-scale feature extraction. Despite promising results, most studies relied on private datasets and lacked external validation, highlighting challenges to reproducibility.