ArticleScientific data2022
Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- Medical Spine Sagittal MRI Dataset for Segmentation and Foraminal Stenosis detection.Scientific data · 2026Article
- Segment Anything Model (SAM) and Medical SAM (MedSAM) for Lumbar Spine MRI.Sensors (Basel, Switzerland) · 2025Article
- SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.European radiology · 2025Article
- A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research.Journal of magnetic resonance imaging : JMRI · 2024Review
- An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification.Scientific data · 2023Article
- Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database.Scientific data · 2022Article
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Authors and funding
7 authors.
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Abstract
Magnetic resonance imaging (MRI) is widely utilized for diagnosing and monitoring of spinal disorders. For a number of applications, particularly those related to quantitative MRI, an essential step towards achieving reliable and objective measurements is the segmentation of the examined structures. Performed manually, such process is time-consuming and prone to errors, posing a bottleneck to its clinical applicability. A more efficient analysis would be achieved by automating a segmentation process. However, routine spine MRI acquisitions pose several challenges for achieving robust and accurate segmentations, due to varying MRI acquisition characteristics occurring in data acquired from different sites. Moreover, heterogeneous annotated datasets, collected from multiple scanners with different pulse sequence protocols, are limited. Thus, we present a manually segmented lumbar spine MRI database containing a wide range of data obtained from multiple scanners and pulse sequences, with segmentations of lumbar vertebral bodies and intervertebral discs. The database is intended for the use in developing and testing of automated lumbar spine segmentation algorithms in multi-domain scenarios.
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