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Validation of a semiautomated spinal cord segmentation method
Mohamed-Mounir El Mendili1, Raphaël Chen, Brice Tiret
1Inserm U678, UPMC Univ Paris 6 UMR-S 678, Laboratoire d'imagerie fonctionnelle, Paris, France; Univ Paris 11, IFR 49, Institut fédératif de recherche en imagerie neurofonctionnelle, DSV/I2BM Neurospin, Gif-sur-Yvette, France.
Purpose:
To validate semiautomated spinal cord segmentation in healthy subjects and patients with neurodegenerative diseases and trauma.
Materials And Methods:
Forty-nine healthy subjects, as well as 29 patients with amyotrophic lateral sclerosis, 19 with spinal muscular atrophy, and 14 with spinal cord injuries were studied. Cord area was measured from T2 -weighted 3D turbo spin echo images (cord levels from C2 to T9) using the semiautomated segmentation method of Losseff et al (Brain [1996] 119(Pt 3):701-708), compared with manual segmentation. Reproducibility was evaluated using the inter- and intraobserver coefficient of variation (CoV). Accuracy was assessed using the Dice similarity coefficient (DSC). Robustness to initialization was assessed by simulating modifications to the contours drawn manually prior to segmentation.
Results:
Mean interobserver CoV was 4.00% for manual segmentation (1.90% for Losseff's method) in the cervical region and 5.62% (respectively 2.19%) in the thoracic region. Mean intraobserver CoV was 2.34% for manual segmentation (1.08% for Losseff's method) in the cervical region and 2.35% (respectively 1.34%) in the thoracic region. DSC was high (0.96) in both cervical and thoracic regions. DSC remained higher than 0.8 even when modifying initial contours by 50%.
Conclusion:
The semiautomated segmentation method showed high reproducibility and accuracy in measuring spinal cord area.
