The spinal cord is a vital organ that serves as the only communication link between the brain and the various parts of the body. It is vulnerable to traumatic spinal cord injury and various diseases such as tumors, infections, inflammatory diseases and degenerative diseases. The exact segmentation and localization of the spinal cord are essential to effective clinical management of such conditions. In recent years, due to the advances in imaging technology, the structure of internal organs and tissues can be captured accurately, and various abnormalities are diagnosed based on scanned images. In this paper, we present an unsupervised segmentation method that automatically extracts the spinal canal in the sagittal plane of magnetic resonance (MR) images. This segmentation method based on a novel saliency-driven attention model and a standard active contour model requires no human intervention and no training. Experiments based on 60 patients' data show that this procedure performs segmentation robustly, achieving the Dice's similarity index of 0.71 between the segmentation by our model and reference segmentation, as compared to the Dice's similarity index of 0.90 between two observers.
['Jaehan Koh', 'Peter D. Scott', 'Vipin Chaudhary', 'Gurmeet Dhillon']
An automatic segmentation method of the spinal canal from clinical MR images based on an attention model and an active contour model