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Abstract #0255

PARTIAL VOLUME ESTIMATION IN MULTIPLE SCLEROSIS LESION SEGMENTATION

Mário João Fartaria 1,2,3, Alexandra Şorega4, Tobias Kober1,2,3, Gunnar Krueger5, Cristina Granziera6,7, Alexis Roche1,2,3, and Meritxell Bach Cuadra2,3,8

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), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Department of Radiology, Valais Hospital, Sion, Switzerland, 5Siemens Medical Solutions USA, Boston, MA, United States, 6Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States, 7Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 8Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland

Partial volume (PV) is the effect of having a mixture of tissues present within a voxel. This effect occurs in tissue borders and affects small structures such as small multiple sclerosis (MS) lesions. Ignoring PV effects in volumetry may lead to significant estimation errors. Here, we propose a novel automated MS lesion segmentation technique that takes PV effects into account. The proposed method shows higher accuracy in terms of lesion volume estimation compared to a manually segmented ground truth as well as significant improvement in detection of small lesions, also in comparison to two software packages for MS lesion segmentation.

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