Dataset Information

Lumbar spine segmentation in MR images: a dataset and a public benchmark.

ABSTRACT: This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.

SUBMITTER: van der Graaf JW

PROVIDER: S-EPMC10908819 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Lumbar spine segmentation in MR images: a dataset and a public benchmark.

van der Graaf Jasper W JW van Hooff Miranda L ML Buckens Constantinus F M CFM Rutten Matthieu M van Susante Job L C JLC Kroeze Robert Jan RJ de Kleuver Marinus M van Ginneken Bram B Lessmann Nikolas N

Scientific data 20240302 1

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic se ...[more]

PMID: 38431692

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Project description:PurposeTo characterize the inter- and intraobserver variability of qualitative, non-disk contour degenerative findings of the lumbar spine at magnetic resonance (MR) imaging.Materials and methodsThe case accrual method used to perform this institutional review board-approved, HIPAA-compliant retrospective study was the random selection of 111 interpretable MR examination cases of subjects from the Spine Patient Outcomes Research Trial. The subjects were aged 18-87 years (mean, 53 years +/- 16 [standard deviation]). Four independent readers rated the cases according to defined criteria. A subsample of 40 MR examination cases was selected for reevaluation at least 1 month later. The following findings were assessed: spondylolisthesis, disk degeneration, marrow endplate abnormality (Modic changes), posterior anular hyperintense zone (HIZ), and facet arthropathy. Inter- and intraobserver agreement in rating the data was summarized by using weighted kappa statistics.ResultsInterobserver agreement was good (kappa = 0.66) in rating disk degeneration and moderate in rating spondylolisthesis (kappa = 0.55), Modic changes (kappa = 0.59), facet arthropathy (kappa = 0.54), and posterior HIZ (kappa = 0.44). Interobserver agreement in rating the extent of Modic changes was moderate: kappa Values were 0.43 for determining superior anteroposterior extent, 0.47 for determining superior craniocaudal extent, 0.57 for determining inferior anteroposterior extent, and 0.48 for determining inferior craniocaudal extent. Intraobserver agreement was good in rating spondylolisthesis (kappa = 0.66), disk degeneration (kappa = 0.74), Modic changes (kappa = 0.64), facet arthropathy (kappa = 0.69), and posterior HIZ (kappa = 0.67). Intraobserver agreement in rating the extent of Modic changes was moderate, with kappa values of 0.54 for superior anteroposterior, 0.60 for inferior anteroposterior, 0.50 for superior craniocaudal, and 0.60 for inferior craniocaudal extent determinations.ConclusionThe interpretation of general lumbar spine MR characteristics has sufficient reliability to warrant the further evaluation of these features as potential prognostic indicators.

Project description:ObjectivesIntroducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo spin echo images.Material and methodsThis local ethics committee-approved study utilized a public dataset (train/test 179/39 subjects, 137 female), a German National Cohort (NAKO) subset (train/test 1412/65 subjects, mean age 53, 694 female), and an in-house dataset (test 10 subjects, mean age 70, 5 female). SPINEPS is a semantic segmentation model, followed by a sliding window approach utilizing a second model to create instance masks from the semantic ones. Segmentation evaluation metrics included the Dice score and average symmetrical surface distance (ASSD). Statistical significance was assessed using the Wilcoxon signed-rank test.ResultsOn the public dataset, SPINEPS outperformed a nnUNet baseline on every structure and metric (e.g., an average over vertebra instances: dice 0.933 vs 0.911, p < 0.001, ASSD 0.21 vs 0.435, p < 0.001). SPINEPS trained on automated annotations of the NAKO achieves an average global Dice score of 0.918 on the combined NAKO and in-house test split. Adding the training data from the public dataset outperforms this (average instance-wise Dice score over the vertebra substructures 0.803 vs 0.778, average global Dice score 0.931 vs 0.918).ConclusionSPINEPS offers segmentation of 14 spinal structures in T2w sagittal images. It provides a semantic mask and an instance mask separating the vertebrae and intervertebral discs. This is the first publicly available algorithm to enable this segmentation.Key pointsQuestion No publicly available automatic approach can yield semantic and instance segmentation masks for the whole spine (including posterior elements) in T2-weighted sagittal TSE images. Findings Segmenting semantically first and then instance-wise outperforms a baseline trained directly on instance segmentation. The developed model produces high-resolution MRI segmentations for the whole spine. Clinical relevance This study introduces an automatic approach to whole spine segmentation, including posterior elements, in arbitrary fields of view T2w sagittal MR images, enabling easy biomarker extraction, automatic localization of pathologies and degenerative diseases, and quantifying analyses as downstream research.

Dataset Information

Lumbar spine segmentation in MR images: a dataset and a public benchmark.

Publications

Lumbar spine segmentation in MR images: a dataset and a public benchmark.

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