The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
Abstrak
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.
Penulis (36)
Tyler J. Richards
Adam E. Flanders
Errol Colak
Luciano M. Prevedello
Robyn L. Ball
Felipe Kitamura
John Mongan
Maryam Vazirabad
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
Akses Cepat
- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓