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Abstract #0488

Toward Fully Automated Assessment of the Central Vein Sign Using Deep Learning

Till Huelnhagen1,2,3, Omar Al Louzi4, Mário João Fartaria1,2,3, Lynn Daboul4, Pietro Maggi5,6, Cristina Granziera7,8,9, Meritxell Bach Cuadra2,3,10, Jonas Richiardi2, Daniel S Reich4, Tobias Kober1,2,3, and Pascal Sati4,11
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, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 6Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium, 7Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 8Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 9Research Center for Clinical Neuroimmunology and Neuroscience (RC2NB) Basel, University Hospital Basel and University of Basel, Basel, Switzerland, 10Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), University of Lausanne, Lausanne, Switzerland, 11Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States

The fraction of white matter lesions exhibiting the central vein sign (CVS) has shown promise as a biomarker in the diagnosis of multiple sclerosis. As manual CVS assessment is not clinically feasible, automated solutions have been proposed to perform this task. A deep-learning-based method called “CVSnet” demonstrated effective and accurate discrimination of MS from its mimics but required manual pre-selection. This work extends CVSnet to allow fully automated CVS assessment without manual interaction. High-quality, expert-reviewed segmentations of almost 6300 lesions were used for training and testing. The proposed method achieved accuracies between 75% and 80% in an unseen testing set.

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