LSMA-PQR: A Comprehensive Dataset of Lumbar Spine Multi-View Annotations with Pfirrmann Grading, Quantitative Measurements, and Structured Radiological Reports

Automated analysis of lumbar spine MRI remains constrained by the absence of a comprehensive dataset integrating pixel-level anatomy, quantitative markings, and radiological reports within a unified framework. We present LSMA-PQR, a clinically validated lumbar spine MRI dataset comprising $$N = 515$$ N = 515 patients with dual-plane, dual-sequence imaging (axial and sagittal T1/T2-weighted; 4120 images) and comprehensive multi-modal annotations. Pixel-level segmentation masks for seven anatomical structures are provided in multiple interoperable formats with verified cross-format spatial consistency (mean IoU = 0.994 ± 0.008 = 0.994 ± 0.008 ). Beyond anatomy, the dataset integrates quantitative disc markings including 1545 Pfirrmann degeneration grades (L3-L4, L4-L5, L5-S1) and intervertebral disc height measurements with pixel-coordinate provenance, as well as structured radiological reports derived from 515 free-text documents through systematic natural language processing that identified and corrected 340 transcription errors across 59% of reports. Clinical validation of the dataset reveals peak degeneration at L5-S1 (71%), a 40% prevalence of severe pathology (Pfirrmann Grade 4–5), and a moderate-to-strong correlation between clinical severity and expert-assigned grades. Baseline experiments on three downstream tasks, multi-plane segmentation (mean Dice = 0.946), disc-aware Pfirrmann grading (quadratic weighted κ κ = 0.51), and structured clinical findings extraction (weighted F1 = 0.59), demonstrate the dataset’s utility for training and evaluating automated lumbar spine analysis systems. LSMA-PQR, available at Mendeley Data , enables previously infeasible research including report-guided supervision, multi-metric cross-validation and integrated modeling of lumbar spine anatomy, degeneration and clinical findings. Data and documentation are released under a CC BY 4.0 license to accelerate translational spine imaging research.