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The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset

Fuente: arXiv

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Main Authors:	Richards, Tyler J., Flanders, Adam E., Colak, Errol, Prevedello, Luciano M., Ball, Robyn L., Kitamura, Felipe, Mongan, John, Vazirabad, Maryam, Lin, Hui-Ming, Kendell, Anne, Kanthawang, Thanat, Angkurawaranon, Salita, Altinmakas, Emre, Dogan, Hakan, Kuriki, Paulo Eduardo de Aguiar, Somasundaram, Arjuna, Ruston, Christopher, Bulja, Deniz, Spahovic, Naida, Sommer, Jennifer, Jiang, Sirui, Farina, Eduardo Moreno Judice de Mattos, Nunes, Eduardo Caminha, Brassil, Michael, McNamara, Megan, Ortiz, Johanna, Peoples, Jacob, Uytana, Vinson L., Kam, Anthony, Dola, Venkata N. S., Murphy, Daniel, Vu, David, Group, Dataset Contributor, Group, Dataset Annotator, Group, Competition Data Notebook, Talbott, Jason F.
Format:	Preprint
Published:	2025
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
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_version_	1866913889106526208
author	Richards, Tyler J. Flanders, Adam E. Colak, Errol Prevedello, Luciano M. Ball, Robyn L. Kitamura, Felipe Mongan, John Vazirabad, Maryam Lin, Hui-Ming Kendell, Anne Kanthawang, Thanat Angkurawaranon, Salita Altinmakas, Emre Dogan, Hakan Kuriki, Paulo Eduardo de Aguiar Somasundaram, Arjuna Ruston, Christopher Bulja, Deniz Spahovic, Naida Sommer, Jennifer Jiang, Sirui Farina, Eduardo Moreno Judice de Mattos Nunes, Eduardo Caminha Brassil, Michael McNamara, Megan Ortiz, Johanna Peoples, Jacob Uytana, Vinson L. Kam, Anthony Dola, Venkata N. S. Murphy, Daniel Vu, David Group, Dataset Contributor Group, Dataset Annotator Group, Competition Data Notebook Talbott, Jason F.
author_facet	Richards, Tyler J. Flanders, Adam E. Colak, Errol Prevedello, Luciano M. Ball, Robyn L. Kitamura, Felipe Mongan, John Vazirabad, Maryam Lin, Hui-Ming Kendell, Anne Kanthawang, Thanat Angkurawaranon, Salita Altinmakas, Emre Dogan, Hakan Kuriki, Paulo Eduardo de Aguiar Somasundaram, Arjuna Ruston, Christopher Bulja, Deniz Spahovic, Naida Sommer, Jennifer Jiang, Sirui Farina, Eduardo Moreno Judice de Mattos Nunes, Eduardo Caminha Brassil, Michael McNamara, Megan Ortiz, Johanna Peoples, Jacob Uytana, Vinson L. Kam, Anthony Dola, Venkata N. S. Murphy, Daniel Vu, David Group, Dataset Contributor Group, Dataset Annotator Group, Competition Data Notebook Talbott, Jason F.
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_09162
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset Richards, Tyler J. Flanders, Adam E. Colak, Errol Prevedello, Luciano M. Ball, Robyn L. Kitamura, Felipe Mongan, John Vazirabad, Maryam Lin, Hui-Ming Kendell, Anne Kanthawang, Thanat Angkurawaranon, Salita Altinmakas, Emre Dogan, Hakan Kuriki, Paulo Eduardo de Aguiar Somasundaram, Arjuna Ruston, Christopher Bulja, Deniz Spahovic, Naida Sommer, Jennifer Jiang, Sirui Farina, Eduardo Moreno Judice de Mattos Nunes, Eduardo Caminha Brassil, Michael McNamara, Megan Ortiz, Johanna Peoples, Jacob Uytana, Vinson L. Kam, Anthony Dola, Venkata N. S. Murphy, Daniel Vu, David Group, Dataset Contributor Group, Dataset Annotator Group, Competition Data Notebook Talbott, Jason F. Image and Video Processing Computer Vision and Pattern Recognition 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.
title	The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
topic	Image and Video Processing Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2506.09162