Abstract #0255

PARTIAL VOLUME ESTIMATION IN MULTIPLE SCLEROSIS LESION SEGMENTATION

Mário João Fartaria ^1,2,3, Alexandra Şorega⁴, Tobias Kober^1,2,3, Gunnar Krueger⁵, Cristina Granziera^6,7, Alexis Roche^1,2,3, and Meritxell Bach Cuadra^2,3,8

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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