Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm

Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm Publisher Pubmed

Sabaghian S^{1, 2} ; Dehghani H^{2, 3} ; Batouli SAH^{2, 4} ; Khatibi A^{5, 6} ; Oghabian MA^{2, 3}

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1. Department of Software, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
2. Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
3. Department of Medical Physics and Biomedical Engineering, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran
4. Department of Neuroscience and Addiction studies, School of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran
5. Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, United Kingdom
6. Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom

Source: Spinal Cord Published:2020

Abstract

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 < 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. Conclusions: The 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. © 2020, The Author(s), under exclusive licence to International Spinal Cord Society.

Style	Citing Format
MLA	Sabaghian S, et al.. "Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm." Spinal Cord, vol. 58, no. 7, 2020, pp. 811-820.
APA	Sabaghian S, Dehghani H, Batouli SAH, Khatibi A, Oghabian MA (2020). Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm. Spinal Cord, 58(7), 811-820.
Chicago	Sabaghian S, Dehghani H, Batouli SAH, Khatibi A, Oghabian MA. "Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm." Spinal Cord 58, no. 7 (2020): 811-820.
Harvard	Sabaghian S et al. (2020) 'Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm', Spinal Cord, 58(7), pp. 811-820.
Vancouver	Sabaghian S, Dehghani H, Batouli SAH, Khatibi A, Oghabian MA. Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm. Spinal Cord. 2020;58(7):811-820.
BibTex	@article{ author = {Sabaghian S and Dehghani H and Batouli SAH and Khatibi A and Oghabian MA}, title = {Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm}, journal = {Spinal Cord}, volume = {58}, number = {7}, pages = {811-820}, year = {2020} }
RIS	TY - JOUR AU - Sabaghian S AU - Dehghani H AU - Batouli SAH AU - Khatibi A AU - Oghabian MA TI - Fully Automatic 3D Segmentation of the Thoracolumbar Spinal Cord and the Vertebral Canal From T2-Weighted Mri Using K-Means Clustering Algorithm JO - Spinal Cord VL - 58 IS - 7 SP - 811 EP - 820 PY - 2020 ER -

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