Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm

Study design Method development. Objectives To develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images. Setting University-based laboratory, Tehran, Iran. Methods T2 structural volumes acquired from the spinal c...

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Bibliographic Details
Published in:Spinal cord 2020-07, Vol.58 (7), p.811-820
Main Authors: Sabaghian, Sahar, Dehghani, Hamed, Batouli, Seyed Amir Hossein, Khatibi, Ali, Oghabian, Mohammad Ali
Format: Article
Language:English
Subjects:
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Algorithms
Anatomy
Biomedical and Life Sciences
Biomedicine
Cluster analysis
Clustering
Human Physiology
Image acquisition
Image contrast
Image processing
Image segmentation
Magnetic resonance imaging
Methods
Metric space
Neurochemistry
Neuropsychology
Neurosciences
Spinal cord
Three dimensional imaging
Vector quantization
Vertebrae
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Summary:Study design Method development. Objectives To develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images. Setting University-based laboratory, Tehran, Iran. Methods T2 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. Results The 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  
ISSN:1362-4393
1476-5624
DOI:10.1038/s41393-020-0429-3