Sahar Sabaghian1,2, Hamed Dehghani2,3, Seyed Amir Hossein Batouli2,4, Ali Khatibi5,6, Mohammad Ali Oghabian7,8. 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, UK. 6. Centre for Human Brain Health, University of Birmingham, Birmingham, UK. 7. Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran. oghabian@sina.tums.ac.ir. 8. Department of Medical Physics and Biomedical Engineering, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran. oghabian@sina.tums.ac.ir.
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.
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.
Authors: Regina Schlaeger; Nico Papinutto; Valentina Panara; Carolyn Bevan; Iryna V Lobach; Monica Bucci; Eduardo Caverzasi; Jeffrey M Gelfand; Ari J Green; Kesshi M Jordan; William A Stern; H-Christian von Büdingen; Emmanuelle Waubant; Alyssa H Zhu; Douglas S Goodin; Bruce A C Cree; Stephen L Hauser; Roland G Henry Journal: Ann Neurol Date: 2014-08-21 Impact factor: 10.422
Authors: Eva M Kesenheimer; Maria Janina Wendebourg; Matthias Weigel; Claudia Weidensteiner; Tanja Haas; Laura Richter; Laura Sander; Antal Horvath; Muhamed Barakovic; Philippe Cattin; Cristina Granziera; Oliver Bieri; Regina Schlaeger Journal: Front Neurol Date: 2021-03-25 Impact factor: 4.003