The Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg)

This work describes a publicly available dataset, the Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg), consisting of 1,255 cervical spine magnetic resonance imaging (MRI) examinations from 1,232 patients collected from the Duke University Health System. CSpineSeg also includes expert manual semantic segmentations of vertebral bodies and intervertebral discs for 481 patients. This dataset aims to provide a resource for training and evaluation of deep learning segmentation models and facilitate cervical spine research. Along with the dataset, we present a deep learning segmentation model which could be used as a benchmark in cervical spine segmentation tasks. Our segmentation model achieves a Dice Coefficient of 0.916, demonstrating the feasibility of utilizing CSpineSeg to train segmentation models.

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Publication Details

Journal
Scientific Data
Published
2025-10-27
DOI
https://doi.org/10.1038/s41597-025-05975-w
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

The Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg)

Maciej A. Mazurowski, Michael D. Malinzak, Roy Colglazier, Longfei Zhou et al.
Scientific Data
Medical Imaging and Analysis
article

The Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg)

Maciej A. Mazurowski, Michael D. Malinzak, Roy Colglazier, Longfei Zhou, Walter F. Wiggins, Evan Calabrese, Hanxue Gu, Jay Willhite, Jikai Zhang, Austin Dixon
article en

Abstract

This work describes a publicly available dataset, the Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg), consisting of 1,255 cervical spine magnetic resonance imaging (MRI) examinations from 1,232 patients collected from the Duke University Health System. CSpineSeg also includes expert manual semantic segmentations of vertebral bodies and intervertebral discs for 481 patients. This dataset aims to provide a resource for training and evaluation of deep learning segmentation models and facilitate cervical spine research. Along with the dataset, we present a deep learning segmentation model which could be used as a benchmark in cervical spine segmentation tasks. Our segmentation model achieves a Dice Coefficient of 0.916, demonstrating the feasibility of utilizing CSpineSeg to train segmentation models.

Scientific DataVol. 12(1)
Duke University (US), Duke Medical Center (US), Gannon University (US)
Openalex Percentile: Top 70%
Medical Imaging and Analysis
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The Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg) — Maciej A. Mazurowski, Michael D. Malinzak, et al. · Scientific Data (2025) | TGRS Research Map | TGRS