The spinal cord is a key component of the central nervous system, acting as a relay to convey information between the brain and the rest of the body. A number of diseases affect the spinal cord, including multiple sclerosis (MS). MS affects more than 240 Canadians per 100,000, and is suspected of shrinking the spinal cord. In fact, recent studies have shown a strong correlation between spinal cord atrophy and disability related to the advancement of the disease. Spinal cord analysis, conducted with magnetic resonance imaging (MRI), is an important tool for detecting and measuring disease progression. This requires cross-sectional segmentation of the image, where specific points that correspond to the spinal cord are identified and measured over time. Most current methods require some manual intervention by radiologists; this is time-consuming and increases variability in the measurements. Chris McIntosh creates software for accurately analyzing tubular structures in the body – such as blood vessels and airways – using MRI and computed tomography. His current focus is on employing MRI to accurately measure and analyze spinal cord atrophy in patients with MS. Building on a preliminary study on automatic spinal cord segmentation, McIntosh is fine tuning the technology through additional validation, ensuring the results correspond with clinical measurements. He will then segment larger data sets with minimal user-interaction and perform analysis to see if the findings correlate with disease progression. A fully-automatic, computerized system would reduce variability seen with manual intervention, resulting in more accurate and useful spinal cord analysis. It also has the potential to free up radiologists’ time for other clinical work.