OptiC: Robust and Automatic Spinal Cord Localization on a Large Variety of MRI Data Using a Distance Transform Based Global Optimization

Localizing the center of the spinal cord on MR images is a critical step toward fully automated and robust quantitative analysis, which is essential to achieve clinical utilization. While automatic localization of the spinal cord might appear as a simple task, that has already been addressed extensively, it is much more challenging to achieve this across the large variation in MRI contrasts, field of view, resolutions and pathologies. In this study, we introduce a novel method, called “OptiC”, to automatically and robustly localize the spinal cord on a large variety of MRI data. Starting from a localization map computed by a linear Support Vector Machine trained with Histogram of Oriented Gradient features, the center of the spinal cord is localized by solving an optimization problem, that introduces a trade-off between the localization map and the cord continuity along the superior-inferior axis. The OptiC algorithm features an efficient search (with a linear complexity in the number of voxels) and ensures the global minimum is reached. OptiC was compared to a recently-published method based on the Hough transform using a broad range of MRI data, involving 13 different centers, 3 contrasts (\(T_2\)-weighted n=278, \(T_1\)-weighted n=112 and \(T_2^*\)-weighted n=263), with a total of 441 subjects, including 133 patients with traumatic and neurodegenerative diseases. Overall, OptiC was able to find 98.5% of the gold-standard centerline coverage, with a mean square error of 1.21 mm, suggesting that OptiC could reliably be used for subsequent analyses tasks, such as cord segmentation, opening the door to more robust analysis in patient population.