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Robust and automatic spinal cord detection on multiple MRI contrasts using machine learning

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Gros, Charley | de Leener, Benjamin | Martin, Allan R. | Fehlings, Michael G. | Callot, Virginie | Stikov, Nikola | Cohen-Adad, Julien

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International audience. Detecting the spinal cord on a large variety of MRI data is challenging but essential for the automation of quantitative analysis pipelines. For the past few years, machine learning algorithms have outperformed most unsupervised image processing methods. The present study investigates the performance of two different machine learning algorithms, Convolutional Neural Networks (CNN) and Support Vector Machine (SVM), on MRI data from different vendors, with a variety of pathology, contrast, resolution and FOV. Results suggest strong performance of the CNN approach, opening the door to application in multi-center analysis pipelines.

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