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The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset

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arxiv 2506.09162 v1 pith:4ZEOQA2M submitted 2025-06-10 eess.IV cs.CV

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset

Tyler J. Richards , Adam E. Flanders , Errol Colak , Luciano M. Prevedello , Robyn L. Ball , Felipe Kitamura , John Mongan , Maryam Vazirabad
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Hui-Ming Lin Anne Kendell Thanat Kanthawang Salita Angkurawaranon Emre Altinmakas Hakan Dogan Paulo Eduardo de Aguiar Kuriki Arjuna Somasundaram Christopher Ruston Deniz Bulja Naida Spahovic Jennifer Sommer Sirui Jiang Eduardo Moreno Judice de Mattos Farina Eduardo Caminha Nunes Michael Brassil Megan McNamara Johanna Ortiz Jacob Peoples Vinson L. Uytana Anthony Kam Venkata N.S. Dola Daniel Murphy David Vu Dataset Contributor Group Dataset Annotator Group Competition Data Notebook Group Jason F. Talbott
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classification eess.IV cs.CV
keywords spinedatasetlumbardegenerativersnaimagingclassificationsociety
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was created for the RSNA 2024 Lumbar Spine Degenerative Classification competition where competitors developed deep learning models to grade degenerative changes in the lumbar spine. The degree of spinal canal, subarticular recess, and neural foraminal stenosis was graded at each intervertebral disc level in the lumbar spine. The images were annotated by expert volunteer neuroradiologists and musculoskeletal radiologists from the RSNA, American Society of Neuroradiology, and the American Society of Spine Radiology. This dataset aims to facilitate research and development in machine learning and lumbar spine imaging to lead to improved patient care and clinical efficiency.

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  1. A multi-agent system for spine MRI report generation from multi-sequence imaging

    cs.CV 2026-06 unverdicted novelty 5.0

    SpineAgent combines multi-sequence MRI embeddings from DINOv3 encoders with 37 specialized agents and an end-to-end Medical Report Agent to achieve SOTA automated spine MRI report generation on a large clinical dataset.