Abstract #0199

Improved Automated Central Vein Sign Assessment by Multi-Level Classification

Till Huelnhagen^1,2,3, Omar al Louzi^4,5, Lynn Daboul⁴, Jonas Richiardi², Daniel S. Reich⁴, Tobias Kober^1,2,3, and Pascal Sati⁵

¹Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, ²Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ³Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁴Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, United States, ⁵Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Synopsis

Central vein sign (CVS) assessment has shown potential to improve differential diagnosis in multiple sclerosis, but automating this task remains non-trivial. As human inter-rater agreement was reported to improve by separating the tasks of lesion exclusion and CVS assessment, we hypothesized that this could also benefit automated CVS assessment. To test this hypothesis, we implemented a novel multi-level classifier for automated CVS assessment and trained and evaluated it in more than 9400 expert-reviewed lesions. The new approach outperforms previous methods, achieving per-class accuracies of 76%–83% in an unseen testing set and >90% accuracy to identify MS cases.

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