Automated Diagnosis of Neural Foraminal Stenosis Using Synchronized Superpixels Representation

Neural foramina stenosis (NFS), as a common spine disease, affects \(80\,\%\) of people. Clinical diagnosis by physicians’ manual segmentation is inefficient and laborious. Automated diagnosis is highly desirable but faces the class overlapping problem derived from the diverse shape and size. In this paper, a fully automated diagnosis approach is proposed for NFS. It is based on a newly proposed synchronized superpixels representation (SSR) model where a highly discriminative feature space is obtained for accurately and easily classifying neural foramina into normal and stenosed classes. To achieve it, class labels (0:normal,1:stenosed) are integrated to guide manifold alignment which correlates images from the same class, so that intra-class difference is reduced and the inter-class margin are maximized. The overall result reaches a high accuracy (\(98.52\,\%\)) in 110 mid-sagittal MR spine images collected from 110 subjects. Hence, with our approach, an efficient and accurate clinical tool is provided to greatly reduce the burden of physicians and ensure the timely treatment of NFS.