ArticleSpinal cord2020
Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm.
Article in Spinal cord, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 25 citations in OpenAlex.
- 3D U-net for volumetric segmentation of spinal cord MRI in degenerative disorders.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.Scientific reports · 2025Article
- Fully Automated Segmentation of Cervical Spinal Cord in Sagittal MR Images Using Swin-Unet Architectures.Journal of clinical medicine · 2025Article
- An unbiased method to partition diverse neuronal responses into functional ensembles reveals interpretable population dynamics during innate social behavior.bioRxiv : the preprint server for biology · 2024Article
- DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.Frontiers in psychiatry · 2024Article
- NAMSTCD: A Novel Augmented Model for Spinal Cord Segmentation and Tumor Classification Using Deep Nets.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Auto-Segmentation of Spinal Cord Internal Structure of Diffusion Tensor Imaging in Cervical Spondylotic Myelopathy.Diagnostics (Basel, Switzerland) · 2023Article
- K-Means Clustering Algorithm-Based Functional Magnetic Resonance for Evaluation of Regular Hemodialysis on Brain Function of Patients with End-Stage Renal Disease.Computational and mathematical methods in medicine · 2022Article
- Nanoscopic subcellular imaging enabled by ion beam tomography.Nature communications · 2021Article
- Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system.NeuroImage · 2021Review
- Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage.Journal of healthcare engineering · 2021Article
- Spine Medical Image Segmentation Based on Deep Learning.Journal of healthcare engineering · 2021Article
- Normalization of Spinal Cord Total Cross-Sectional and Gray Matter Areas as Quantified With Radially Sampled Averaged Magnetization Inversion Recovery Acquisitions.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designMethod development.
objectivesTo develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images.
settingUniversity-based laboratory, Tehran, Iran.
methodsT2 structural volumes acquired from the spinal cord of 20 uninjured volunteers on a 3T MR scanner. We proposed an automatic method for spinal cord segmentation based on the K-means clustering algorithm in 3D images and compare our results with two available segmentation methods (PropSeg, DeepSeg) implemented in the Spinal Cord Toolbox. Dice and Hausdorff were used to compare the results of our method (K-Seg) with the manual segmentation, PropSeg, and DeepSeg.
resultsThe accuracy of our automatic segmentation method for T2-weighted images was significantly better or similar to the SCT methods, in terms of 3D DC (p < 0.001). The 3D DCs were respectively (0.81 ± 0.04) and Hausdorff Distance (12.3 ± 2.48) by the K-Seg method in contrary to other SCT methods for T2-weighted images.
conclusionsThe output with similar protocols showed that K-Seg results match the manual segmentation better than the other methods especially on the thoracolumbar levels in the spinal cord due to the low image contrast as a result of poor SNR in these areas.
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