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Abstract #0667

The Role of Partial Volume Modelling in Longitudinal Automated Multiple Sclerosis Lesion Segmentation

Mário João Fartaria1,2,3, Tobias Kober1,2,3, Cristina Granziera4,5,6, and Meritxell Bach Cuadra2,3,7

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, 5Neuroimmunology Unit, Neurology, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 6Neurology Department and Neuroimaging Laboratory, Basel University Hospital, Basel, Switzerland, 7Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland

Longitudinal analyses in Multiple Sclerosis are often performed to assess disease progression and evaluate treatment response. The number of new and enlarged lesions as well as total lesion volume variations over time are imaging biomarkers used in MS follow-up assessment. Here, we evaluate the performance of an in-house prototype algorithm for lesion detection and volume estimation in a longitudinal scenario. Our algorithm can be run with or without partial volume modelling. Both detection and volume estimation improved using the partial volume model with respect to manual delineations, especially in small lesions and at lesion borders.

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