Improved Automated Central Vein Sign Assessment by Multi-Level Classification
Till Huelnhagen1,2,3, Omar al Louzi4,5, Lynn Daboul4, Jonas Richiardi2, Daniel S. Reich4, Tobias Kober1,2,3, 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
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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