Multiple Sclerosis lesions in the spinal cord are associated with more debilitative disease outcomes and have predictive value for prognosis and diagnosis. However, these lesions are difficult to detect from MRI scans and this process is susceptible to inter-rater and intra-rater variability. Machine Learning techniques have the ability to assist in this problem. We propose a Convolutional Neural Network that can perform accurate identification and segmentation of MS lesions in the spinal cord. This method achieves high overlap with the segmentations of attending radiologists and is robust to imaging artifacts, showcasing the potential to be a tool for clinical practice.